[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_237":238,"_239":240},"routes/_app.x.$handle.$name",{"_104":103,"_32":45,"_113":91,"_27":114,"_13":14,"_115":-5,"_11":116,"_117":118,"_119":120,"_121":122,"_123":124,"_20":-5,"_125":126,"_127":128,"_129":25,"_130":131,"_132":60,"_133":-5,"_134":-5,"_135":136},"routes/_app.x.$handle.$name.branch.$branch.version.$version.diff",{"_11":12,"_13":14,"_15":16,"_17":-5,"_18":-5},"project",{"_43":44,"_32":45,"_38":46,"_47":-5,"_48":25,"_49":50,"_51":50,"_52":53,"_54":55,"_18":56,"_57":58,"_59":60,"_61":-5,"_62":-5,"_63":64,"_65":66,"_67":68,"_69":70,"_71":72,"_73":-5,"_74":75,"_76":75,"_77":78,"_79":78,"_80":78,"_81":75,"_82":75,"_83":84,"_85":86,"_87":88,"_89":25},"version",25,"changes",[19],"aiChatInfo","userId",{"_20":21,"_22":23,"_24":25,"_26":25,"_27":28,"_29":28},"file",{"_30":31,"_32":28,"_33":34,"_35":37,"_38":39,"_40":14,"_41":42,"_24":25},"previous",{"_30":31,"_32":28,"_33":34,"_35":36},"deleted",false,"deletedHere","path","vapi.ts","latestPath","fileId","019ec8e3-8fe9-75a2-9f23-f5bf6aa88136","name","fileType","script","content","// Thin Vapi REST client — BYOK: every function takes the caller's own key.\n// Nothing is read from env; this is a public multi-tenant server.\n//\n// dispatch-my-human DOES use Vapi's own LLM brain (native model), unlike the\n// puppet sibling call-my-human whose custom-llm always returned \"\". The agent\n// runs the whole conversation itself for low latency, then Vapi's analysis\n// produces a structured report we hand back to the dispatcher.\n\nconst VAPI_BASE = \"https://api.vapi.ai\";\n\nexport interface DispatchOpts {\n vapiKey: string;\n phoneNumberId: string;\n number: string;\n firstMessage: string;\n systemPrompt: string;\n questions: string[];\n model: string; // e.g. \"gpt-4o\"\n maxDurationSeconds: number;\n origin: string; // this val's https origin\n tenant: string; // short hash of the vapi key — routes webhooks back to the right feed\n}\n\n// Structured report schema: objective + one answer per required question + followups.\n// The required questions are baked into the schema descriptions (not a separate\n// prompt) so the extractor always sees them — Vapi feeds it the transcript by default.\nfunction structuredSchema(questions: string[]) {\n const qList = questions.length\n ? \" The agent was told to get answers to these specific questions: \" +\n questions.map((q, i) => `(${i + 1}) ${q}`).join(\"; \") +\n \". Provide one entry per question, in that order.\"\n : \"\";\n return {\n type: \"object\",\n properties: {\n objective_met: {\n type: \"boolean\",\n description: \"True if the call accomplished its stated objective.\",\n },\n answers: {\n type: \"array\",\n description:\n \"One entry for each question the agent was asked to get answered, with the human's answer.\" + qList,\n items: {\n type: \"object\",\n properties: {\n question: { type: \"string\", description: \"The question the agent needed answered.\" },\n answer: {\n type: \"string\",\n description: \"The human's answer, paraphrased or verbatim. Use 'not answered' if never resolved.\",\n },\n },\n required: [\"question\", \"answer\"],\n },\n },\n followups: {\n type: \"string\",\n description:\n \"Anything the human raised, requested, or asked that the dispatcher should act on after the call. Empty string if none.\",\n },\n },\n required: [\"objective_met\", \"answers\"],\n };\n}\n\nexport async function createDispatchCall(opts: DispatchOpts) {\n const body = {\n phoneNumberId: opts.phoneNumberId,\n customer: { number: opts.number },\n assistant: {\n firstMessage: opts.firstMessage,\n // A REAL brain this time: Vapi runs the LLM in-stack (lowest latency, no\n // round-trip back to the dispatcher). The endCall tool lets it hang up\n // itself once the objective is met.\n model: {\n provider: \"openai\",\n model: opts.model,\n temperature: 0.6,\n messages: [{ role: \"system\", content: opts.systemPrompt }],\n tools: [\n { type: \"endCall\" },\n // The escape hatch (ASYNC). When the agent hits a question outside its\n // brief it calls this; /tool records it and returns immediately so the\n // agent keeps talking. The operator's answer is pushed into the live\n // call later via provide_answer -> add-message.\n {\n type: \"function\",\n function: {\n name: \"ask_operator\",\n description:\n \"Ask your operator (the AI assistant that dispatched you) something you cannot answer from your briefing — the MOMENT the human asks something off-brief; never guess. This is ASYNC: it returns immediately, so DON'T wait — keep the conversation going. The operator's answer arrives shortly as a message in the conversation, and you relay it then.\",\n parameters: {\n type: \"object\",\n properties: {\n question: {\n type: \"string\",\n description: \"The human's question, phrased clearly and completely for your operator.\",\n },\n },\n required: [\"question\"],\n },\n },\n // Async: /tool returns immediately, so no long hold. One quick bridge line.\n server: { url: `${opts.origin}/tool/${opts.tenant}`, secret: opts.tenant, timeoutSeconds: 20 },\n messages: [\n {\n type: \"request-start\",\n content: \"Let me loop in my operator on that — I'll keep us going and share what they say the moment it lands.\",\n },\n ],\n },\n ],\n },\n // No transcriber override — use Vapi's default (what templates/vapi-phone-call\n // uses). The explicit deepgram nova-2 override added latency and produced\n // duplicated/cumulative transcript lines. Native models get a default STT.\n // Vapi-native voice — what the dashboard \"test call\" uses, tuned for telephony\n // and bundled (no extra provider key). Much better on a phone than openai/shimmer.\n voice: { provider: \"vapi\", voiceId: \"Elliot\", version: 2 },\n server: { url: `${opts.origin}/webhook/${opts.tenant}`, secret: opts.tenant },\n serverMessages: [\"transcript\", \"status-update\", \"end-of-call-report\", \"conversation-update\"],\n // The human pauses to think; the agent answers immediately, so dead air\n // means a genuinely dropped/empty call — 30s is a safe cutoff.\n silenceTimeoutSeconds: 30,\n maxDurationSeconds: opts.maxDurationSeconds,\n // Auto-summarize + extract the structured report when the call ends.\n // Schema only — Vapi's default extractor prompt already receives the\n // transcript; a custom {{transcript}} message silently broke extraction.\n analysisPlan: {\n structuredDataPlan: {\n enabled: true,\n schema: structuredSchema(opts.questions),\n },\n },\n },\n };\n\n console.log(\"vapi: dispatching call →\", opts.number, \"tenant\", opts.tenant, \"model\", opts.model);\n const res = await fetch(`${VAPI_BASE}/call`, {\n method: \"POST\",\n headers: { Authorization: `Bearer ${opts.vapiKey}`, \"Content-Type\": \"application/json\" },\n body: JSON.stringify(body),\n });\n const data = await res.json();\n if (!res.ok) {\n console.log(\"vapi: dispatch FAILED\", res.status, JSON.stringify(data).slice(0, 500));\n throw new Error(`Vapi ${res.status}: ${JSON.stringify(data)}`);\n }\n console.log(\"vapi: call created\", data.id, \"controlUrl:\", data.monitor?.controlUrl ? \"present\" : \"MISSING\");\n return data as { id: string; status?: string; monitor?: { controlUrl?: string; listenUrl?: string } };\n}\n\nexport async function control(controlUrl: string, payload: Record) {\n const res = await fetch(controlUrl, {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify(payload),\n });\n const text = await res.text();\n console.log(\"vapi control:\", payload.type, \"→\", res.status, text.slice(0, 200));\n return { status: res.status, body: text };\n}\n\n// Optional supervisor barge-in: inject a verbatim line over the live call.\n// Docs use `content`; the SDK's SayMessage type uses `message`. Try both.\nexport async function sayOnCall(controlUrl: string, text: string, endCallAfterSpoken = false) {\n const base: Record = { type: \"say\", content: text };\n if (endCallAfterSpoken) base.endCallAfterSpoken = true;\n let r = await control(controlUrl, base);\n if (r.status >= 400) {\n const alt: Record = { type: \"say\", message: text };\n if (endCallAfterSpoken) alt.endCallAfterSpoken = true;\n r = await control(controlUrl, alt);\n }\n return r;\n}\n\nexport async function endCallOnVapi(controlUrl: string) {\n return control(controlUrl, { type: \"end-call\" });\n}\n\nexport async function getCallLive(vapiKey: string, callId: string) {\n const res = await fetch(`${VAPI_BASE}/call/${callId}`, {\n headers: { Authorization: `Bearer ${vapiKey}` },\n });\n const data = await res.json();\n if (!res.ok) throw new Error(`Vapi GET /call ${res.status}: ${JSON.stringify(data).slice(0, 300)}`);\n return data;\n}\n","// Thin Vapi REST client — BYOK: every function takes the caller's own key.\n// Nothing is read from env; this is a public multi-tenant server.\n//\n// dispatch-my-human DOES use Vapi's own LLM brain (native model), unlike the\n// puppet sibling call-my-human whose custom-llm always returned \"\". The agent\n// runs the whole conversation itself for low latency, then Vapi's analysis\n// produces a structured report we hand back to the dispatcher.\n\nconst VAPI_BASE = \"https://api.vapi.ai\";\n\nexport interface DispatchOpts {\n vapiKey: string;\n phoneNumberId: string;\n number: string;\n firstMessage: string;\n systemPrompt: string;\n questions: string[];\n model: string; // e.g. \"gpt-4o\"\n maxDurationSeconds: number;\n origin: string; // this val's https origin\n tenant: string; // short hash of the vapi key — routes webhooks back to the right feed\n}\n\n// Structured report schema: objective + one answer per required question + followups.\n// The required questions are baked into the schema descriptions (not a separate\n// prompt) so the extractor always sees them — Vapi feeds it the transcript by default.\nfunction structuredSchema(questions: string[]) {\n const qList = questions.length\n ? \" The agent was told to get answers to these specific questions: \" +\n questions.map((q, i) => `(${i + 1}) ${q}`).join(\"; \") +\n \". Provide one entry per question, in that order.\"\n : \"\";\n return {\n type: \"object\",\n properties: {\n objective_met: {\n type: \"boolean\",\n description: \"True if the call accomplished its stated objective.\",\n },\n answers: {\n type: \"array\",\n description:\n \"One entry for each question the agent was asked to get answered, with the human's answer.\" + qList,\n items: {\n type: \"object\",\n properties: {\n question: { type: \"string\", description: \"The question the agent needed answered.\" },\n answer: {\n type: \"string\",\n description: \"The human's answer, paraphrased or verbatim. Use 'not answered' if never resolved.\",\n },\n },\n required: [\"question\", \"answer\"],\n },\n },\n followups: {\n type: \"string\",\n description:\n \"Anything the human raised, requested, or asked that the dispatcher should act on after the call. Empty string if none.\",\n },\n },\n required: [\"objective_met\", \"answers\"],\n };\n}\n\nexport async function createDispatchCall(opts: DispatchOpts) {\n const body = {\n phoneNumberId: opts.phoneNumberId,\n customer: { number: opts.number },\n assistant: {\n firstMessage: opts.firstMessage,\n // A REAL brain this time: Vapi runs the LLM in-stack (lowest latency, no\n // round-trip back to the dispatcher). The endCall tool lets it hang up\n // itself once the objective is met.\n model: {\n provider: \"openai\",\n model: opts.model,\n temperature: 0.6,\n messages: [{ role: \"system\", content: opts.systemPrompt }],\n tools: [\n { type: \"endCall\" },\n // The escape hatch (ASYNC). When the agent hits a question outside its\n // brief it calls this; /tool records it and returns immediately so the\n // agent keeps talking. The operator's answer is pushed into the live\n // call later via provide_answer -> add-message.\n {\n type: \"function\",\n function: {\n name: \"ask_operator\",\n description:\n \"Ask your operator (the AI assistant that dispatched you) something you cannot answer from your briefing — the MOMENT the human asks something off-brief; never guess. This is ASYNC: it returns immediately, so DON'T wait — keep the conversation going. The operator's answer arrives shortly as a message in the conversation, and you relay it then.\",\n parameters: {\n type: \"object\",\n properties: {\n question: {\n type: \"string\",\n description: \"The human's question, phrased clearly and completely for your operator.\",\n },\n },\n required: [\"question\"],\n },\n },\n // Async: /tool returns immediately, so no long hold. One quick bridge line.\n server: { url: `${opts.origin}/tool/${opts.tenant}`, secret: opts.tenant, timeoutSeconds: 20 },\n messages: [\n {\n type: \"request-start\",\n content: \"Let me loop in my operator on that — I'll keep us going and share what they say the moment it lands.\",\n },\n ],\n },\n ],\n },\n // No transcriber override — use Vapi's default (what templates/vapi-phone-call\n // uses). The explicit deepgram nova-2 override added latency and produced\n // duplicated/cumulative transcript lines. Native models get a default STT.\n // Vapi-native voice — what the dashboard \"test call\" uses, tuned for telephony\n // and bundled (no extra provider key). Much better on a phone than openai/shimmer.\n voice: { provider: \"vapi\", voiceId: \"Elliot\", version: 2 },\n server: { url: `${opts.origin}/webhook/${opts.tenant}`, secret: opts.tenant },\n serverMessages: [\"transcript\", \"status-update\", \"end-of-call-report\", \"conversation-update\"],\n // The human pauses to think; the agent answers immediately, so dead air\n // means a genuinely dropped/empty call — 30s is a safe cutoff.\n silenceTimeoutSeconds: 30,\n maxDurationSeconds: opts.maxDurationSeconds,\n // Auto-summarize + extract the structured report when the call ends.\n // Schema only — Vapi's default extractor prompt already receives the\n // transcript; a custom {{transcript}} message silently broke extraction.\n analysisPlan: {\n structuredDataPlan: {\n enabled: true,\n schema: structuredSchema(opts.questions),\n },\n },\n },\n };\n\n console.log(\"vapi: dispatching call →\", opts.number, \"tenant\", opts.tenant, \"model\", opts.model);\n const res = await fetch(`${VAPI_BASE}/call`, {\n method: \"POST\",\n headers: { Authorization: `Bearer ${opts.vapiKey}`, \"Content-Type\": \"application/json\" },\n body: JSON.stringify(body),\n });\n const data = await res.json();\n if (!res.ok) {\n console.log(\"vapi: dispatch FAILED\", res.status, JSON.stringify(data).slice(0, 500));\n throw new Error(`Vapi ${res.status}: ${JSON.stringify(data)}`);\n }\n console.log(\"vapi: call created\", data.id, \"controlUrl:\", data.monitor?.controlUrl ? \"present\" : \"MISSING\");\n return data as { id: string; status?: string; monitor?: { controlUrl?: string; listenUrl?: string } };\n}\n\nexport async function control(controlUrl: string, payload: Record) {\n const res = await fetch(controlUrl, {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify(payload),\n });\n const text = await res.text();\n console.log(\"vapi control:\", payload.type, \"→\", res.status, text.slice(0, 200));\n return { status: res.status, body: text };\n}\n\n// Optional supervisor barge-in: inject a verbatim line over the live call.\n// Docs use `content`; the SDK's SayMessage type uses `message`. Try both.\nexport async function sayOnCall(controlUrl: string, text: string, endCallAfterSpoken = false) {\n const base: Record = { type: \"say\", content: text };\n if (endCallAfterSpoken) base.endCallAfterSpoken = true;\n let r = await control(controlUrl, base);\n if (r.status >= 400) {\n const alt: Record = { type: \"say\", message: text };\n if (endCallAfterSpoken) alt.endCallAfterSpoken = true;\n r = await control(controlUrl, alt);\n }\n return r;\n}\n\nexport async function endCallOnVapi(controlUrl: string) {\n return control(controlUrl, { type: \"end-call\" });\n}\n\n// Async escape hatch: push the operator's answer into the LIVE call's conversation\n// and trigger the agent to speak. 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Nothing comes from env.\n//\n// Unlike the puppet sibling (call-my-human), here the Vapi agent has a REAL\n// brain and runs the whole conversation itself. Primary loop:\n// dispatch_call → hand off a brief + objective + questions\n// await_report → long-poll until it hangs up, get the structured result\n// listen / say are optional supervisor hooks (eavesdrop / barge in).\n\nimport * as db from \"./db.ts\";\nimport { addMessageToCall, createDispatchCall, endCallOnVapi, getCallLive, sayOnCall } from \"./vapi.ts\";\n\nexport interface Ctx {\n origin: string;\n vapiKey: string | null;\n phoneNumberId: string | null;\n humanNumber: string | null;\n model: string; // LLM the agent's brain runs on (default gpt-4o)\n tenant: string | null; // short hash of vapiKey\n}\n\nconst CONNECT_HELP =\n \"Missing Vapi credentials. Put them in your MCP connection URL: /mcp?vapi_key=&phone_number_id=&human_number=%2B15551234567 (the + must be URL-encoded as %2B). Get the key and phone number at dashboard.vapi.ai.\";\n\nfunction creds(ctx: Ctx): { vapiKey: string; tenant: string } {\n if (!ctx.vapiKey || !ctx.tenant) throw new Error(CONNECT_HELP);\n return { vapiKey: ctx.vapiKey, tenant: ctx.tenant };\n}\n\nconst ACTIVE = [\"starting\", \"queued\", \"ringing\", \"in-progress\", \"forwarding\"];\n\nasync function requireActiveCall(tenant: string): Promise {\n const call = await db.latestCall(tenant);\n if (!call) throw new Error(\"No call yet — use dispatch_call first.\");\n if (!ACTIVE.includes(call.status)) {\n throw new Error(\n `Latest call ${call.id} is '${call.status}'${call.ended_reason ? ` (${call.ended_reason})` : \"\"} — start a new one with dispatch_call.`,\n );\n }\n return call;\n}\n\nconst fmt = (e: db.EventRow) => ({\n t: e.created_at.slice(11, 19),\n type: e.type,\n role: e.role,\n content: e.content,\n});\n\n// Build the agent's system prompt from the dispatch brief. This IS the brain —\n// it carries the whole call, so be explicit about who it is and what \"done\" means.\nfunction buildSystemPrompt(opts: {\n objective: string;\n context: string;\n questions: string[];\n callerIdentity: string;\n maxMinutes: number;\n}): string {\n const qBlock = opts.questions.length\n ? \"Before you hang up, get a clear answer to EACH of these:\\n\" +\n opts.questions.map((q, i) => ` ${i + 1}. ${q}`).join(\"\\n\")\n : \"There are no specific required questions beyond the objective above.\";\n const ctxBlock = opts.context.trim()\n ? `Context you've been briefed with (use it; don't read it aloud verbatim):\\n${opts.context.trim()}`\n : \"You have not been given extra context beyond the objective.\";\n\n return `You are ${opts.callerIdentity}. You are placing an OUTBOUND phone call to a person on behalf of your operator (the AI assistant / system that dispatched you), who is busy and needs something from them. You called THEM — you are not their assistant waiting for requests.\n\nYOUR OBJECTIVE:\n${opts.objective.trim()}\n\n${ctxBlock}\n\n${qBlock}\n\nHOW TO TALK:\n- Sound like a real person on the phone: short, natural sentences, one idea at a time. No monologues.\n- Open by quickly saying who you are and why you're calling, then get to the point.\n- Listen and react to what they actually say. Don't plow through a script.\n- If it's a bad time, offer to have your operator follow up later, then wrap up.\n\nIF THEY ASK SOMETHING OUTSIDE YOUR BRIEF:\n- Do NOT guess, improvise, or make something up. The moment the human asks something your objective and context don't clearly cover, call the ask_operator function with their exact question.\n- This is ASYNC — you do NOT wait on hold. After sending the question you don't have to fill the air: it's perfectly fine to just say you're checking and sit with a short, comfortable pause, OR pick the conversation back up naturally if there's something real to say. Don't force small talk.\n- Your operator's answer will arrive a little later as a \"[Message from your operator]\" in the conversation. The moment it appears, naturally tell the human \"oh — I just heard back from my operator,\" relay the answer in your own words, then continue.\n- You can have more than one question out at once. Use this only for genuine gaps you can't fill from your briefing.\n\nWHEN YOU'RE DONE:\n- NEVER end the call while the human is still talking or mid-thought. Wait for a clear, complete answer and a natural pause.\n- Only after you have answers to ALL the required questions (or they say they have to go) should you wrap up. First ask \"anything else before I let you go?\" — if they're still talking, keep listening; do not hang up.\n- Then briefly confirm what you heard, thank them, and use the endCall function.\n- Keep the whole call under ${opts.maxMinutes} minutes, but getting complete answers matters more than speed — don't rush them off.`;\n}\n\nexport const TOOLS = [\n {\n name: \"dispatch_call\",\n description:\n \"Dispatch a voice agent to phone your human and handle a conversation autonomously, then hang up. UNLIKE a puppet, this agent has a real brain: you hand it a brief once and it carries the whole call itself with low latency — you do NOT drive it turn by turn. Give it a clear `objective`, any `context` it needs, and the specific `questions` you want answered. After it returns, call `await_report` to block until the call ends and collect the structured result (summary + per-question answers + followups). For most check-ins this is the only tool you need besides await_report.\",\n inputSchema: {\n type: \"object\",\n properties: {\n objective: {\n type: \"string\",\n description: \"The goal of the call in plain language, e.g. 'Confirm whether we should ship the release tonight or wait for QA.'\",\n },\n context: {\n type: \"string\",\n description: \"Everything the agent should know to handle the call well — the situation, relevant facts, constraints. Dump freely; it won't read it aloud.\",\n },\n questions: {\n type: \"array\",\n items: { type: \"string\" },\n description: \"Specific questions you need answered. Each becomes a field in the returned report.\",\n },\n number: {\n type: \"string\",\n description: \"E.164 number to dial, e.g. +15551234567. Defaults to the human_number from your connection URL.\",\n },\n first_message: {\n type: \"string\",\n description: \"The exact opening line the agent speaks on pickup. Optional — a sensible default is used.\",\n },\n caller_identity: {\n type: \"string\",\n description: \"Who the agent should present as, e.g. 'Charlie's AI assistant'. Optional.\",\n },\n max_minutes: {\n type: \"number\",\n description: \"Hard cap on call length in minutes (default 10).\",\n },\n },\n required: [\"objective\"],\n },\n run: async (args: Record, ctx: Ctx) => {\n const { vapiKey, tenant } = creds(ctx);\n const phoneNumberId = ctx.phoneNumberId || String(args.phone_number_id || \"\");\n if (!phoneNumberId) {\n throw new Error(\"phone_number_id missing — add &phone_number_id= to your connection URL.\");\n }\n const prev = await db.latestCall(tenant);\n if (prev && ACTIVE.includes(prev.status)) {\n let stillActive = false;\n try {\n const live = await getCallLive(vapiKey, prev.id);\n if (live.status && live.status !== prev.status) {\n await db.setCallStatus(prev.id, live.status, live.endedReason ?? null);\n }\n stillActive = ACTIVE.includes(live.status);\n } catch (e) {\n console.log(\"dispatch_call: live status check failed, allowing new call:\", String(e).slice(0, 120));\n }\n if (stillActive) throw new Error(`Call ${prev.id} is still active — end_call it first.`);\n }\n const number = String(args.number || ctx.humanNumber || \"\");\n if (!number) {\n throw new Error(\n \"No number to dial — pass `number`, or add &human_number=%2B15551234567 to your connection URL.\",\n );\n }\n const objective = String(args.objective || \"\").trim();\n if (!objective) throw new Error(\"`objective` is required — tell the agent what the call is for.\");\n const context = String(args.context || \"\");\n const questions = Array.isArray(args.questions) ? args.questions.map(String).filter(Boolean) : [];\n const callerIdentity = String(args.caller_identity || \"an AI voice assistant\");\n const maxMinutes = Math.min(Math.max(Number(args.max_minutes ?? 10), 1), 15);\n const firstMessage = String(\n args.first_message ||\n \"Hi! This is your AI assistant calling — I've got a quick thing to run by you, do you have a sec?\",\n );\n const systemPrompt = buildSystemPrompt({ objective, context, questions, callerIdentity, maxMinutes });\n\n const call = await createDispatchCall({\n vapiKey,\n phoneNumberId,\n number,\n firstMessage,\n systemPrompt,\n questions,\n model: ctx.model,\n maxDurationSeconds: maxMinutes * 60,\n origin: ctx.origin,\n tenant,\n });\n const controlUrl = call.monitor?.controlUrl ?? \"\";\n await db.insertCall(tenant, call.id, controlUrl, number);\n await db.addEvent(tenant, call.id, \"status\", \"system\", `dispatching ${number} · objective: ${objective.slice(0, 120)}`);\n return {\n call_id: call.id,\n status: call.status ?? \"queued\",\n dialing: number,\n questions_tracked: questions.length,\n watch_url: `${ctx.origin}/?t=${tenant}`,\n next:\n \"Call `await_report` now and keep calling it. It returns either needs_answer:true (the agent hit an off-brief question — reply with `provide_answer`, then await_report again) or done:true with the summary + answers. Optional: `listen` / `say` to eavesdrop or barge in.\",\n };\n },\n },\n {\n name: \"await_report\",\n description:\n \"Block until the dispatched call finishes, then return the structured outcome: a summary, an answer to each question you asked, anything the human raised for follow-up, and the full transcript. Long-polls up to wait_seconds — keep calling it (it's cheap) until you get done:true. While done:false the call is still in progress or the post-call analysis is still being written.\",\n inputSchema: {\n type: \"object\",\n properties: {\n wait_seconds: { type: \"number\", description: \"Max seconds to long-poll this call (1-25, default 20).\" },\n },\n },\n run: async (args: Record, ctx: Ctx) => {\n const { vapiKey, tenant } = creds(ctx);\n const call = await db.latestCall(tenant);\n if (!call) throw new Error(\"No call yet — use dispatch_call first.\");\n const wait = Math.min(Math.max(Number(args.wait_seconds ?? 20), 1), 25);\n const deadline = Date.now() + wait * 1000;\n\n const buildReport = async (status: string, endedReason: string | null) => {\n const analysis = await db.latestEventOfType(call.id, \"analysis\");\n const summaryEv = await db.latestEventOfType(call.id, \"report\");\n let structured: Record | null = null;\n if (analysis?.content) {\n try {\n structured = JSON.parse(analysis.content);\n } catch { /* leave null */ }\n }\n const transcript = (await db.transcriptLines(call.id)).map((e) => ({ role: e.role, text: e.content }));\n return {\n done: true,\n call_status: status,\n ended_reason: endedReason,\n summary: summaryEv?.content ?? null,\n objective_met: structured?.objective_met ?? null,\n answers: structured?.answers ?? null,\n followups: structured?.followups ?? null,\n transcript,\n ...(structured ? {} : { note: \"Call ended but structured analysis isn't ready yet — call await_report once more in a few seconds.\" }),\n };\n };\n\n while (true) {\n // Escape hatch: if the agent is waiting on YOU for an off-brief question,\n // surface it immediately so you can provide_answer.\n const esc = await db.pendingEscalation(tenant);\n if (esc) {\n return {\n done: false,\n needs_answer: true,\n escalation: { id: esc.id, question: esc.question },\n note:\n \"The agent hit a question outside its brief and is waiting on YOU. Answer it with provide_answer({ text }) — it will relay your answer to the human — then call await_report again.\",\n };\n }\n let current = await db.getCall(call.id);\n const terminal = current && !ACTIVE.includes(current.status);\n // Done = call ended AND the post-call analysis has landed (or we've waited it out).\n if (terminal) {\n const analysis = await db.latestEventOfType(call.id, \"analysis\");\n if (analysis) return await buildReport(current!.status, current!.ended_reason);\n }\n if (Date.now() > deadline) {\n // Sync from Vapi in case a webhook was missed.\n try {\n const live = await getCallLive(vapiKey, call.id);\n if (live?.status && live.status !== current?.status) {\n await db.setCallStatus(call.id, String(live.status), (live.endedReason as string) ?? null);\n current = await db.getCall(call.id);\n }\n } catch (e) {\n console.log(\"await_report: live status check failed:\", String(e).slice(0, 120));\n }\n const stillTerminal = current && !ACTIVE.includes(current.status);\n if (stillTerminal) return await buildReport(current!.status, current!.ended_reason);\n return {\n done: false,\n call_status: current?.status ?? \"unknown\",\n ...(current?.live_partial ? { hearing_now: current.live_partial } : {}),\n note: \"Call still in progress — call await_report again to keep waiting.\",\n };\n }\n await new Promise((r) => setTimeout(r, 1000));\n }\n },\n },\n {\n name: \"provide_answer\",\n description:\n \"Answer a question the agent escalated to you mid-call (when await_report returns needs_answer:true). Your text is handed back to the agent as the result of its ask_operator call, and it relays it to the human in its own words. Keep it brief and factual — it will be spoken aloud. After answering, call await_report again to keep waiting for the report or the next question.\",\n inputSchema: {\n type: \"object\",\n properties: {\n text: {\n type: \"string\",\n description: \"The answer to relay, in plain language. It's spoken to the human, so keep it concise.\",\n },\n },\n required: [\"text\"],\n },\n run: async (args: Record, ctx: Ctx) => {\n const { tenant } = creds(ctx);\n const esc = await db.pendingEscalation(tenant);\n if (!esc) {\n throw new Error(\"No pending question from the agent right now — call await_report to see if it asks one.\");\n }\n const text = String(args.text ?? \"\").trim();\n if (!text) throw new Error(\"`text` is required — provide the answer to relay to the human.\");\n const call = await db.getCall(esc.call_id);\n if (!call?.control_url) {\n throw new Error(\"No control URL for this call — can't deliver the answer into the live call.\");\n }\n // Push the answer into the live conversation; triggerResponse makes the agent\n // announce it (\"just heard back from my operator...\") and relay it in its words.\n const inject =\n `[Message from your operator] About the human's question \"${esc.question}\": ${text}\\n\\n` +\n \"Now tell the human you just heard back from your operator and relay this naturally in your own words, then carry on.\";\n const r = await addMessageToCall(call.control_url, inject);\n await db.answerEscalation(esc.id, text);\n await db.addEvent(tenant, esc.call_id, \"answer\", \"assistant\", text);\n if (r.status >= 400) {\n throw new Error(`Vapi add-message rejected (${r.status}): ${r.body.slice(0, 200)}`);\n }\n return { ok: true, answered: esc.question, next: \"Delivered into the live call — the agent will announce it. Call await_report again to continue.\" };\n },\n },\n {\n name: \"listen\",\n description:\n \"Optional: eavesdrop on a live dispatched call. Long-polls for new events (the human's speech, the agent's lines, status changes) up to wait_seconds. Use this if you want to watch the conversation unfold in realtime; you do NOT need it for the normal dispatch→await_report flow.\",\n inputSchema: {\n type: \"object\",\n properties: {\n wait_seconds: { type: \"number\", description: \"Max seconds to wait (1-25, default 20).\" },\n },\n },\n run: async (args: Record, ctx: Ctx) => {\n const { vapiKey, tenant } = creds(ctx);\n const call = await db.latestCall(tenant);\n if (!call) throw new Error(\"No call yet — use dispatch_call first.\");\n const wait = Math.min(Math.max(Number(args.wait_seconds ?? 20), 1), 25);\n const deadline = Date.now() + wait * 1000;\n let cursor = call.cursor;\n while (true) {\n const events = await db.eventsAfter(call.id, cursor);\n const maxId = events.length ? events[events.length - 1].id : cursor;\n if (events.length) {\n await db.setCursor(call.id, maxId);\n const current = await db.getCall(call.id);\n const over = current && !ACTIVE.includes(current.status);\n return {\n events: events.map(fmt),\n call_status: current?.status ?? call.status,\n ...(over ? { note: `Call is over (${current?.ended_reason ?? current?.status}). Use await_report for the result.` } : {}),\n };\n }\n const current = await db.getCall(call.id);\n if (current && !ACTIVE.includes(current.status)) {\n return {\n events: [],\n call_status: current.status,\n note: `Call is over (${current.ended_reason ?? current.status}). Use await_report for the result.`,\n };\n }\n if (Date.now() > deadline) {\n try {\n const live = await getCallLive(vapiKey, call.id);\n if (live?.status && live.status !== current?.status) {\n await db.setCallStatus(call.id, String(live.status), (live.endedReason as string) ?? null);\n }\n } catch (e) {\n console.log(\"listen: live status check failed:\", String(e).slice(0, 120));\n }\n const synced = await db.getCall(call.id);\n if (synced && !ACTIVE.includes(synced.status)) {\n return {\n events: [],\n call_status: synced.status,\n note: `Call is over (${synced.ended_reason ?? synced.status}). Use await_report for the result.`,\n };\n }\n return {\n events: [],\n call_status: synced?.status ?? \"unknown\",\n ...(synced?.live_partial ? { hearing_now: synced.live_partial } : {}),\n note: \"Nothing new yet — the agent is handling it. listen again, or just await_report.\",\n };\n }\n await new Promise((r) => setTimeout(r, 800));\n }\n },\n },\n {\n name: \"say\",\n description:\n \"Optional supervisor barge-in: speak a verbatim line over the live call yourself (TTS), e.g. to correct the agent or relay a fact it didn't have. Normally unnecessary — the agent drives the call. Set end_call_after true to hang up after the line plays.\",\n inputSchema: {\n type: \"object\",\n properties: {\n text: { type: \"string\", description: \"What to say, verbatim.\" },\n end_call_after: { type: \"boolean\", description: \"Hang up after this line finishes playing.\" },\n },\n required: [\"text\"],\n },\n run: async (args: Record, ctx: Ctx) => {\n const { tenant } = creds(ctx);\n const call = await requireActiveCall(tenant);\n if (!call.control_url) throw new Error(\"No controlUrl stored for this call — cannot inject speech.\");\n const r = await sayOnCall(call.control_url, String(args.text), Boolean(args.end_call_after));\n await db.addEvent(tenant, call.id, \"say\", \"assistant\", String(args.text));\n if (r.status >= 400) throw new Error(`Vapi control rejected say (${r.status}): ${r.body.slice(0, 200)}`);\n return { ok: true, vapi_status: r.status, next: \"Use listen to hear their response, or await_report for the final result.\" };\n },\n },\n {\n name: \"get_status\",\n description:\n \"Snapshot of your latest call: live status straight from Vapi plus the recent event feed. Does not consume listen's cursor. Includes a watch_url where you can see the live transcript.\",\n inputSchema: { type: \"object\", properties: {} },\n run: async (_args: Record, ctx: Ctx) => {\n const { vapiKey, tenant } = creds(ctx);\n const call = await db.latestCall(tenant);\n if (!call) return { note: \"No calls yet.\", watch_url: `${ctx.origin}/?t=${tenant}` };\n let live: Record | null = null;\n try {\n live = await getCallLive(vapiKey, call.id);\n if (live?.status && live.status !== call.status) {\n await db.setCallStatus(call.id, String(live.status), (live.endedReason as string) ?? null);\n }\n } catch (e) {\n live = { error: String(e) };\n }\n const events = await db.recentEvents(call.id, 15);\n return {\n call_id: call.id,\n number: call.number,\n db_status: call.status,\n vapi_status: live?.status ?? null,\n ended_reason: live?.endedReason ?? call.ended_reason ?? null,\n started_at: call.created_at,\n watch_url: `${ctx.origin}/?t=${tenant}`,\n recent_events: events.map(fmt),\n };\n },\n },\n {\n name: \"end_call\",\n description: \"Hang up the live call immediately (e.g. to abort a dispatched call). The agent normally ends calls itself.\",\n inputSchema: { type: \"object\", properties: {} },\n run: async (_args: Record, ctx: Ctx) => {\n const { tenant } = creds(ctx);\n const call = await requireActiveCall(tenant);\n const r = await endCallOnVapi(call.control_url);\n await db.addEvent(tenant, call.id, \"status\", \"system\", \"end-call requested\");\n return { ok: r.status < 400, vapi_status: r.status };\n },\n },\n];\n\nexport async function runTool(name: string, args: Record, ctx: Ctx) {\n const tool = TOOLS.find((t) => t.name === name);\n if (!tool) throw new Error(`Unknown tool: ${name}`);\n console.log(\"tool:\", name, \"tenant:\", ctx.tenant ?? \"none\", JSON.stringify(args ?? {}).slice(0, 200));\n return await tool.run(args ?? {}, ctx);\n}\n",[160],"ad3d84ca-8564-4f83-8c1b-6f26e78969cc","mcp.ts","019ec8e3-8fe9-75a2-9f23-eff83960cf88",12,["D",1781486990915],2,"// Minimal remote MCP server: stateless streamable HTTP, plain JSON responses,\n// no SDK. Handles initialize / ping / tools/list / tools/call; notifications get 202.\n\nimport { type Ctx, runTool, TOOLS } from \"./tools.ts\";\n\nconst KNOWN_VERSIONS = [\"2024-11-05\", \"2025-03-26\", \"2025-06-18\"];\n\nconst SERVER_INSTRUCTIONS =\n `dispatch-my-human-escape: dispatch an autonomous voice agent to phone your human and run the call itself — WITH an escape hatch back to you.\nThe agent has a REAL brain and drives the conversation. But when the human asks something outside its brief, it calls for backup and the call PAUSES until YOU answer.\nPrimary loop:\n 1. dispatch_call({ objective, context?, questions?, number?, first_message?, max_minutes? }) — hand off the call.\n 2. await_report({ wait_seconds }) — long-polls. It returns ONE of:\n • needs_answer:true with an { escalation: { question } } — the agent is stuck on an off-brief question. Reply with provide_answer({ text }), then call await_report again.\n • done:true with { summary, objective_met, answers[], followups, transcript } — the call is over.\n 3. provide_answer({ text }) — your answer is fed back to the agent (a \"let me check on that\" plays for the human meanwhile) and it relays it in its own words.\nGive a crisp objective, dump context, and list the questions you want answered. Optional: listen (eavesdrop), say (barge in), get_status, end_call.\nSetup: connection URL needs vapi_key and phone_number_id (and ideally human_number, E.164 with + as %2B) from dashboard.vapi.ai. Brain runs on your Vapi OpenAI model (gpt-4o default; &model= to override).`;\n\nconst rpcResult = (id: unknown, result: unknown) => ({ jsonrpc: \"2.0\", id, result });\nconst rpcError = (id: unknown, code: number, message: string) => ({ jsonrpc: \"2.0\", id, error: { code, message } });\n\nasync function handleOne(msg: any, ctx: Ctx) {\n if (!msg || typeof msg !== \"object\" || typeof msg.method !== \"string\") {\n return rpcError(msg?.id ?? null, -32600, \"invalid request\");\n }\n if (msg.id === undefined) {\n console.log(\"mcp notification:\", msg.method);\n return null; // notifications get no body\n }\n console.log(\"mcp request:\", msg.method, \"tenant:\", ctx.tenant ?? \"none\");\n switch (msg.method) {\n case \"initialize\": {\n const v = msg.params?.protocolVersion;\n return rpcResult(msg.id, {\n protocolVersion: KNOWN_VERSIONS.includes(v) ? v : \"2025-06-18\",\n capabilities: { tools: { listChanged: false } },\n serverInfo: { name: \"dispatch-my-human-escape\", version: \"1.0.0\" },\n instructions: SERVER_INSTRUCTIONS,\n });\n }\n case \"ping\":\n return rpcResult(msg.id, {});\n case \"tools/list\":\n return rpcResult(msg.id, {\n tools: TOOLS.map(({ name, description, inputSchema }) => ({ name, description, inputSchema })),\n });\n case \"tools/call\": {\n try {\n const out = await runTool(msg.params?.name, msg.params?.arguments ?? {}, ctx);\n const text = typeof out === \"string\" ? out : JSON.stringify(out, null, 2);\n return rpcResult(msg.id, { content: [{ type: \"text\", text }], isError: false });\n } catch (e) {\n return rpcResult(msg.id, { content: [{ type: \"text\", text: String(e) }], isError: true });\n }\n }\n default:\n return rpcError(msg.id, -32601, `method not found: ${msg.method}`);\n }\n}\n\nexport async function handleMcp(req: Request, ctx: Ctx): Promise {\n if (req.method !== \"POST\") {\n return new Response(\"call-my-human: POST JSON-RPC here (stateless streamable HTTP MCP). GET / for setup.\", {\n status: 405,\n });\n }\n let body: unknown;\n try {\n body = await req.json();\n } catch {\n return Response.json(rpcError(null, -32700, \"parse error\"), { status: 400 });\n }\n const msgs = Array.isArray(body) ? body : [body];\n const replies: unknown[] = [];\n for (const m of msgs) {\n const r = await handleOne(m, ctx);\n if (r) replies.push(r);\n }\n if (!replies.length) return new Response(null, { status: 202 });\n return Response.json(Array.isArray(body) ? replies : replies[0]);\n}\n",[168],"077f2e7b-8316-490b-8e64-9be22acd8228","main.ts",21,["D",1781490000709],"http","// dispatch-my-human — a PUBLIC remote MCP server that dispatches an autonomous\n// voice agent to phone your human, handle the conversation itself, and report back.\n// Unlike the puppet sibling (call-my-human), the Vapi agent here has a REAL brain:\n// it runs the whole call in-stack (low latency, no per-turn round-trip), then Vapi's\n// post-call analysis returns a structured report.\n// BYOK: connecting users pass their own Vapi creds in the connection URL.\n// No env vars, no server-side keys, no signup.\n//\n// /mcp?vapi_key=…&phone_number_id=…&human_number=%2B1555… ← connect here\n// /api/call/:action same tools as REST (dispatch, report, listen, say, status, end)\n// /webhook/:tenant Vapi server events → SQLite (transcripts + end-of-call analysis)\n// / plain GET = setup instructions; /?t= = live transcript feed\n//\n// A tenant is a short hash of the caller's Vapi key — it scopes call state per user\n// and routes Vapi's webhooks back to the right feed. Keys are never stored.\n\nimport { Hono } from \"npm:hono\";\nimport * as db from \"./db.ts\";\nimport { handleMcp } from \"./mcp.ts\";\nimport { type Ctx, runTool } from \"./tools.ts\";\n\nconst app = new Hono();\napp.onError((err) => Promise.reject(err));\n\nasync function tenantOf(key: string): Promise {\n const d = await crypto.subtle.digest(\"SHA-256\", new TextEncoder().encode(key));\n return [...new Uint8Array(d)].slice(0, 6).map((b) => b.toString(16).padStart(2, \"0\")).join(\"\");\n}\n\nasync function buildCtx(c: any): Promise {\n const q = (k: string) => c.req.query(k) || null;\n const bearer = (c.req.header(\"authorization\") ?? \"\").replace(/^Bearer\\s+/i, \"\") || null;\n const vapiKey = q(\"vapi_key\") ?? bearer;\n return {\n origin: new URL(c.req.url).origin,\n vapiKey,\n phoneNumberId: q(\"phone_number_id\"),\n humanNumber: q(\"human_number\"),\n model: q(\"model\") || \"gpt-4o\", // the agent's brain; override with &model= in the URL\n tenant: vapiKey ? await tenantOf(vapiKey) : null,\n };\n}\n\n// ---------- remote MCP ----------\napp.all(\"/mcp\", async (c) => handleMcp(c.req.raw, await buildCtx(c)));\n\n// ---------- REST mirror of the tools ----------\nconst REST: Record = {\n dispatch: \"dispatch_call\",\n report: \"await_report\",\n answer: \"provide_answer\",\n listen: \"listen\",\n say: \"say\",\n status: \"get_status\",\n end: \"end_call\",\n};\n\napp.all(\"/api/call/:action\", async (c) => {\n const tool = REST[c.req.param(\"action\")];\n if (!tool) return c.json({ error: \"unknown action — use dispatch | report | answer | listen | say | status | end\" }, 404);\n let args: Record = {};\n if (c.req.method === \"POST\") {\n try {\n args = await c.req.json();\n } catch { /* empty body is fine */ }\n }\n try {\n return c.json({ ok: true, result: await runTool(tool, args, await buildCtx(c)) });\n } catch (e) {\n return c.json({ ok: false, error: String(e) }, 400);\n }\n});\n\n// state for the live feed page (tenant id is required — it is unguessable without the key)\napp.get(\"/api/state\", async (c) => {\n const t = c.req.query(\"t\");\n if (!t) return c.json({ error: \"pass ?t= (shown in start_call/get_status results)\" }, 400);\n const call = await db.latestCall(t);\n if (!call) return c.json({ call: null, events: [] });\n const events = await db.recentEvents(call.id, 120);\n return c.json({ call, events });\n});\n\n// ---------- Vapi webhook (per tenant) ----------\napp.post(\"/webhook/:tenant\", async (c) => {\n const tenant = c.req.param(\"tenant\");\n if ((c.req.header(\"x-vapi-secret\") ?? \"\") !== tenant) {\n console.log(\"webhook: bad or missing x-vapi-secret for tenant\", tenant);\n return c.json({ error: \"bad secret\" }, 401);\n }\n const payload = await c.req.json().catch(() => ({}));\n const m = payload?.message ?? {};\n const callId: string = m?.call?.id ?? (await db.latestCall(tenant))?.id ?? \"unknown\";\n console.log(\"webhook:\", tenant, m.type, m.status ?? m.transcriptType ?? \"\", String(m.transcript ?? \"\").slice(0, 120));\n\n const mtype = String(m.type ?? \"\");\n if (mtype.startsWith(\"transcript\")) {\n // type is \"transcript\" or the documented variant 'transcript[transcriptType=\"final\"]'\n const ttype = m.transcriptType ?? (mtype.includes(\"final\") ? \"final\" : \"partial\");\n if (ttype === \"final\") {\n await db.addEvent(tenant, callId, \"transcript\", m.role ?? \"user\", m.transcript ?? \"\");\n await db.setPartial(callId, null);\n } else {\n await db.setPartial(callId, (m.role ?? \"?\") + \": \" + (m.transcript ?? \"\"));\n }\n } else if (mtype === \"conversation-update\") {\n // Fallback transcript source: backfill any user lines the transcript events missed.\n const conv = (m.conversation ?? m.messages ?? []) as Array>;\n const users = conv\n .filter((x) => x.role === \"user\")\n .map((x) => String(x.message ?? x.content ?? \"\"))\n .filter(Boolean);\n if (users.length) {\n const seen = new Set(\n (await db.recentEvents(callId, 80))\n .filter((e) => e.type === \"transcript\" && e.role === \"user\")\n .map((e) => e.content),\n );\n for (const u of users) {\n if (!seen.has(u)) {\n await db.addEvent(tenant, callId, \"transcript\", \"user\", u);\n seen.add(u);\n }\n }\n }\n } else if (mtype === \"status-update\") {\n await db.setCallStatus(callId, m.status ?? \"unknown\", m.endedReason ?? null);\n await db.addEvent(tenant, callId, \"status\", \"system\", String(m.status) + (m.endedReason ? \" (\" + m.endedReason + \")\" : \"\"));\n } else if (mtype === \"end-of-call-report\") {\n // Analysis (summary + structuredData) is computed at end-of-call and rides\n // along in this report. await_report waits for the \"analysis\" event below.\n await db.setCallStatus(callId, \"ended\", m.endedReason ?? null);\n const analysis = m?.analysis ?? {};\n const summary = analysis.summary ?? m.summary ?? \"call ended\";\n await db.addEvent(tenant, callId, \"report\", \"system\", String(summary).slice(0, 1000));\n if (analysis.structuredData !== undefined && analysis.structuredData !== null) {\n await db.addEvent(tenant, callId, \"analysis\", \"system\", JSON.stringify(analysis.structuredData));\n }\n }\n return c.json({});\n});\n\n// ---------- escape hatch: async tool endpoint (per tenant) ----------\n// The agent calls ask_operator; Vapi POSTs here. We record the question and return\n// immediately (no hold) so the agent keeps talking. The operator's answer is pushed\n// into the live call later via provide_answer -> add-message.\napp.post(\"/tool/:tenant\", async (c) => {\n const tenant = c.req.param(\"tenant\");\n const secret = c.req.header(\"x-vapi-secret\") ?? \"\";\n if (secret && secret !== tenant) {\n console.log(\"tool: bad x-vapi-secret for tenant\", tenant);\n return c.json({ error: \"bad secret\" }, 401);\n }\n const payload = await c.req.json().catch(() => ({}));\n const m = payload?.message ?? {};\n const tc = (m.toolCallList ?? [])[0] ?? {};\n const toolCallId: string = tc.id ?? tc.toolCall?.id ?? \"unknown\";\n // Vapi may send arguments as an object OR a JSON string, and under .arguments\n // or .function.arguments depending on shape — handle all of them.\n let rawArgs: unknown = tc.arguments ?? tc.function?.arguments ?? tc.toolCall?.function?.arguments ?? {};\n if (typeof rawArgs === \"string\") {\n try { rawArgs = JSON.parse(rawArgs); } catch { rawArgs = {}; }\n }\n const a = (rawArgs ?? {}) as Record;\n const question = String(a.question ?? a.q ?? \"\").trim() || \"(the agent asked for help but gave no question)\";\n const callId: string = m?.call?.id ?? (await db.latestCall(tenant))?.id ?? \"unknown\";\n console.log(\"tool ask_operator:\", tenant, toolCallId, question.slice(0, 160));\n\n await db.insertEscalation(tenant, callId, toolCallId, question);\n await db.addEvent(tenant, callId, \"escalation\", \"system\", question);\n\n // ASYNC: acknowledge immediately so the agent doesn't go on hold. The operator's\n // real answer is pushed into the live call later via provide_answer -> add-message.\n return c.json({\n results: [{\n toolCallId,\n result:\n \"Sent to your operator. Do NOT wait on hold — keep the conversation going with the human about anything else. Their answer will be delivered into our conversation shortly; when it arrives, naturally tell the human you just heard back, then relay it.\",\n }],\n });\n});\n\n// ---------- landing + live feed ----------\nfunction landing(origin: string): string {\n return `dispatch-my-human — send an agent to phone your human (public remote MCP server)\n\nDispatch an autonomous voice agent to call your human, run the whole\nconversation itself, and hand you back a structured report. You brief it once\n(objective + context + questions); it talks, hangs up, and you collect the\nresult. The agent has a real LLM brain running in-stack, so the call is\nlow-latency and natural — you don't drive it turn by turn.\n\nTools: dispatch_call · await_report · listen · say · get_status · end_call.\nThe headline loop is just dispatch_call → await_report. listen/say are optional\nsupervisor hooks (eavesdrop on, or barge into, a live call).\n\nBring your own keys from https://dashboard.vapi.ai (private key + phone number ID).\nThe agent's brain uses your Vapi account's bundled OpenAI model (gpt-4o by\ndefault; override with &model=). No extra LLM key needed for OpenAI models.\n\nConnect from Claude Code:\n\n claude mcp add --transport http dispatch-my-human \\\\\n \"${origin}/mcp?vapi_key=YOUR_KEY&phone_number_id=YOUR_PHONE_ID&human_number=%2B15551234567\"\n\nNo MCP client? The tools speak plain REST with the same query params:\n POST ${origin}/api/call/dispatch {\"objective\": \"...\", \"context\": \"...\", \"questions\": [\"...\"]}\n POST ${origin}/api/call/report {\"wait_seconds\": 25} (long-polls; use a 35s timeout, repeat until done:true)\n POST ${origin}/api/call/listen {\"wait_seconds\": 25} (optional: eavesdrop live)\n POST ${origin}/api/call/say {\"text\": \"...\", \"end_call_after\": false} (optional: barge in)\n POST ${origin}/api/call/status · POST ${origin}/api/call/end\n\nNotes:\n • human_number is the default number dispatch_call dials — your cell. The +\n MUST be URL-encoded as %2B.\n • Works in any MCP client that takes a URL (claude.ai custom connectors too).\n • Watch a call live: dispatch_call returns a watch_url.\n • Free Vapi numbers are flaky (occasional dead bridges, a DAILY outbound\n limit). Import a Twilio number into Vapi for reliability.\n • Privacy: your key is read per-request and never stored. Call transcripts\n and reports ARE stored in this val's SQLite, scoped to a hash of your key.\n Want full custody? Remix this val — it has no other config.\n\nSibling: dcm31/call-my-human is the puppet version (you drive every word).\nSource + README: https://www.val.town/x/dcm31/dispatch-my-human\n`;\n}\n\nconst PAGE = `\n\n\ndispatch-my-human — live\n\n\n

📞 dispatch-my-human no call

\n\n`;\n\napp.get(\"/\", (c) => {\n if (c.req.query(\"t\")) return c.html(PAGE);\n return c.text(landing(new URL(c.req.url).origin));\n});\n\nexport default app.fetch;\n",[176],"430af59e-db66-4a52-861e-1e7229b36d73","db.ts","019ec8e3-8fe9-75a2-9f23-e7bdd7e0ed3f",3,["D",1781486839919],"// SQLite layer: calls + an append-only event feed per call, scoped by tenant.\n// A tenant is a short hash of the caller's Vapi key — every connecting user\n// gets their own call history on this shared public server.\n\nimport { sqlite } from \"https://esm.town/v/std/sqlite/main.ts\";\n\nawait sqlite.batch([\n `CREATE TABLE IF NOT EXISTS calls (\n id TEXT PRIMARY KEY,\n tenant TEXT,\n control_url TEXT,\n status TEXT DEFAULT 'starting',\n ended_reason TEXT,\n live_partial TEXT,\n cursor INTEGER DEFAULT 0,\n number TEXT,\n created_at TEXT\n )`,\n `CREATE TABLE IF NOT EXISTS events (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n tenant TEXT,\n call_id TEXT,\n type TEXT,\n role TEXT,\n content TEXT,\n created_at TEXT\n )`,\n // Escape hatch: a mid-call question the agent routes to the operator. The\n // blocking /tool endpoint inserts a row and waits for `answer` to be filled.\n `CREATE TABLE IF NOT EXISTS escalations (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n tenant TEXT,\n call_id TEXT,\n tool_call_id TEXT,\n question TEXT,\n answer TEXT,\n created_at TEXT,\n answered_at TEXT\n )`,\n]);\n// Remixed schema may predate the tenant column — add it if missing.\ntry {\n await sqlite.execute(`ALTER TABLE calls ADD COLUMN tenant TEXT`);\n} catch { /* already there */ }\ntry {\n await sqlite.execute(`ALTER TABLE events ADD COLUMN tenant TEXT`);\n} catch { /* already there */ }\n\nconst now = () => new Date().toISOString();\n\nexport interface CallRow {\n id: string;\n tenant: string;\n control_url: string;\n status: string;\n ended_reason: string | null;\n live_partial: string | null;\n cursor: number;\n number: string;\n created_at: string;\n}\n\nexport interface EventRow {\n id: number;\n tenant: string;\n call_id: string;\n type: string;\n role: string;\n content: string;\n created_at: string;\n}\n\nexport async function insertCall(tenant: string, id: string, controlUrl: string, number: string) {\n await sqlite.execute({\n sql: `INSERT INTO calls (id, tenant, control_url, status, cursor, number, created_at)\n VALUES (?, ?, ?, 'starting', 0, ?, ?)`,\n args: [id, tenant, controlUrl, number, now()],\n });\n}\n\nexport async function latestCall(tenant: string): Promise {\n const r = await sqlite.execute({\n sql: `SELECT * FROM calls WHERE tenant = ? ORDER BY created_at DESC LIMIT 1`,\n args: [tenant],\n });\n return (r.rows[0] as unknown as CallRow) ?? null;\n}\n\nexport async function getCall(id: string): Promise {\n const r = await sqlite.execute({ sql: `SELECT * FROM calls WHERE id = ?`, args: [id] });\n return (r.rows[0] as unknown as CallRow) ?? null;\n}\n\nexport async function setCallStatus(id: string, status: string, endedReason?: string | null) {\n await sqlite.execute({\n sql: `UPDATE calls SET status = ?, ended_reason = COALESCE(?, ended_reason) WHERE id = ?`,\n args: [status, endedReason ?? null, id],\n });\n}\n\nexport async function setPartial(id: string, partial: string | null) {\n await sqlite.execute({ sql: `UPDATE calls SET live_partial = ? WHERE id = ?`, args: [partial, id] });\n}\n\nexport async function setCursor(id: string, cursor: number) {\n await sqlite.execute({ sql: `UPDATE calls SET cursor = ? WHERE id = ?`, args: [cursor, id] });\n}\n\nexport async function addEvent(tenant: string, callId: string, type: string, role: string, content: string) {\n await sqlite.execute({\n sql: `INSERT INTO events (tenant, call_id, type, role, content, created_at) VALUES (?, ?, ?, ?, ?, ?)`,\n args: [tenant, callId, type, role, content, now()],\n });\n}\n\nexport async function eventsAfter(callId: string, afterId: number): Promise {\n const r = await sqlite.execute({\n sql: `SELECT * FROM events WHERE call_id = ? AND id > ? ORDER BY id ASC`,\n args: [callId, afterId],\n });\n return r.rows as unknown as EventRow[];\n}\n\nexport async function hasTranscript(callId: string): Promise {\n const r = await sqlite.execute({\n sql: `SELECT 1 FROM events WHERE call_id = ? AND type = 'transcript' LIMIT 1`,\n args: [callId],\n });\n return r.rows.length > 0;\n}\n\nexport async function latestEventOfType(callId: string, type: string): Promise {\n const r = await sqlite.execute({\n sql: `SELECT * FROM events WHERE call_id = ? AND type = ? ORDER BY id DESC LIMIT 1`,\n args: [callId, type],\n });\n return (r.rows[0] as unknown as EventRow) ?? null;\n}\n\nexport async function transcriptLines(callId: string, limit = 200): Promise {\n const r = await sqlite.execute({\n sql: `SELECT * FROM events WHERE call_id = ? AND type = 'transcript' ORDER BY id ASC LIMIT ?`,\n args: [callId, limit],\n });\n return r.rows as unknown as EventRow[];\n}\n\nexport async function recentEvents(callId: string, limit = 100): Promise {\n const r = await sqlite.execute({\n sql: `SELECT * FROM events WHERE call_id = ? ORDER BY id DESC LIMIT ?`,\n args: [callId, limit],\n });\n return (r.rows as unknown as EventRow[]).reverse();\n}\n\n// ---------- escalations (the escape hatch) ----------\n\nexport interface EscalationRow {\n id: number;\n tenant: string;\n call_id: string;\n tool_call_id: string;\n question: string;\n answer: string | null;\n created_at: string;\n answered_at: string | null;\n}\n\nexport async function insertEscalation(\n tenant: string,\n callId: string,\n toolCallId: string,\n question: string,\n): Promise {\n const r = await sqlite.execute({\n sql: `INSERT INTO escalations (tenant, call_id, tool_call_id, question, created_at)\n VALUES (?, ?, ?, ?, ?) RETURNING id`,\n args: [tenant, callId, toolCallId, question, now()],\n });\n return Number((r.rows[0] as unknown as { id: number }).id);\n}\n\nexport async function getEscalation(id: number): Promise {\n const r = await sqlite.execute({ sql: `SELECT * FROM escalations WHERE id = ?`, args: [id] });\n return (r.rows[0] as unknown as EscalationRow) ?? null;\n}\n\n// The oldest still-unanswered question for this tenant (FIFO).\nexport async function pendingEscalation(tenant: string): Promise {\n const r = await sqlite.execute({\n sql: `SELECT * FROM escalations WHERE tenant = ? AND answer IS NULL ORDER BY id ASC LIMIT 1`,\n args: [tenant],\n });\n return (r.rows[0] as unknown as EscalationRow) ?? null;\n}\n\nexport async function answerEscalation(id: number, answer: string) {\n await sqlite.execute({\n sql: `UPDATE escalations SET answer = ?, answered_at = ? WHERE id = ? AND answer IS NULL`,\n args: [answer, now(), id],\n });\n}\n",[183],"b7d7a24f-4d12-49d6-ad29-166dc8694032","README.md","019ec8e3-8fe9-75a2-9f23-e39b934cea1e",1,["D",1781486817181],"# dispatch-my-human — send an agent to phone your human 📞\n\nA **public remote MCP server**: connect it to any AI agent and it can dispatch an *autonomous* voice agent to phone your human, **run the whole conversation itself**, hang up, and hand back a structured report. The sibling [dcm31/call-my-human](https://www.val.town/x/dcm31/call-my-human) is a *puppet* — you drive every word over a dumb pipe, which is high-latency. This one un-lobotomizes the agent: it has a real LLM brain running **inside Vapi's stack**, so there's no per-turn round-trip back to your reasoning loop. Same BYOK design: **no env vars, no server-side keys, no signup**.\n\n```\nyour agent (any MCP client)\n │ dispatch_call({ objective, context, questions }) ← brief it ONCE\n ▼\nthis val ──── POST api.vapi.ai/call (your key) ────▶ Vapi ─── gpt-4o brain ──▶ 📱 you\n ▲ │ │ (runs the call itself,\n │ │ │ ends it with endCall)\n │ └── await_report ◀── end-of-call analysis ──────┘\n └── /webhook/ ◀── transcripts + structured report ──┘\n```\n\nThe agent carries the call: it opens, stays on objective, gets your answers, and uses Vapi's `endCall` tool to hang up. When it ends, Vapi's **analysis plan** auto-extracts a summary + an answer to each question you asked + any follow-ups, and `await_report` returns it.\n\n## The loop\n\nJust two tools for the common case:\n\n1. **`dispatch_call`** `{ objective, context?, questions?, number?, first_message?, caller_identity?, max_minutes? }` — hand off the call.\n2. **`await_report`** `{ wait_seconds }` — long-polls; keep calling it until `done:true`. Returns `{ summary, objective_met, answers[], followups, transcript }`.\n\nOptional supervisor hooks (you usually won't need them):\n\n- **`listen`** — eavesdrop on the live call in realtime.\n- **`say`** — barge in with a verbatim line (e.g. correct the agent or feed it a fact).\n- **`get_status`**, **`end_call`** — snapshot / abort.\n\n## Connect\n\nYou need two values from [dashboard.vapi.ai](https://dashboard.vapi.ai): your **private key** and a **phone number ID** (free Vapi numbers work, US-only).\n\n```sh\nclaude mcp add --transport http dispatch-my-human \\\n \"https://dcm31--019ec8902328734fa4beeb375dc81ac4.web.val.run/mcp?vapi_key=YOUR_KEY&phone_number_id=YOUR_PHONE_ID&human_number=%2B15551234567\"\n```\n\n- `human_number` = your cell, the default callee. **The `+` must be URL-encoded as `%2B`.**\n- The brain runs on your Vapi account's bundled **OpenAI** model (`gpt-4o` default). No extra LLM key needed for OpenAI models. Override with `&model=gpt-4o-mini` (faster) or any Vapi-supported model.\n- Any MCP client that takes a URL works (claude.ai custom connectors included). `Authorization: Bearer ` is accepted in place of the `vapi_key` param.\n- Then tell your agent: *\"if you get stuck, dispatch a call to me and ask.\"*\n\n## Example\n\n> Agent: `dispatch_call({ objective: \"Decide whether to ship the v2 release tonight\", context: \"QA found one minor cosmetic bug, nothing functional. The marketing email is scheduled for 8am.\", questions: [\"Ship tonight or hold for the fix?\", \"If we hold, what's the new target?\"], caller_identity: \"Charlie's AI assistant\" })`\n>\n> …agent calls, talks it through, hangs up…\n>\n> Agent: `await_report({})` → `{ objective_met: true, answers: [{question:\"Ship tonight or hold for the fix?\", answer:\"Ship tonight\"}, {question:\"If we hold, what's the new target?\", answer:\"N/A — shipping\"}], summary: \"...\", followups: \"Wants a Slack ping once it's live.\" }`\n\n## Watching a call live\n\n`dispatch_call` and `get_status` return a `watch_url` (`/?t=`) — a self-refreshing feed of statuses, both-sides transcript, and the final structured report.\n\n## Testing with curl\n\n```sh\nBASE=\"https://dcm31--019ec8902328734fa4beeb375dc81ac4.web.val.run\"\nQ=\"vapi_key=YOUR_KEY&phone_number_id=YOUR_PHONE_ID&human_number=%2B15551234567\"\ncurl -X POST \"$BASE/api/call/dispatch?$Q\" -d '{\"objective\":\"Confirm Friday dinner\",\"questions\":[\"Still on for Friday?\",\"What time?\"]}'\ncurl -X POST \"$BASE/api/call/report?$Q\" -d '{\"wait_seconds\":25}' --max-time 35 # repeat until done:true\n```\n\n## Files\n\n- `main.ts` — routes, per-tenant Vapi webhook (transcripts + end-of-call analysis), landing + live feed\n- `tools.ts` — the tools, shared by MCP and REST; builds the agent's system prompt from your brief\n- `vapi.ts` — Vapi REST client: native-model call creation with the `endCall` tool + structured-data analysis plan\n- `mcp.ts` — minimal stateless streamable-HTTP MCP plumbing (no SDK)\n- `db.ts` — SQLite: tenant-scoped `calls` + append-only `events` (transcripts, report, structured analysis)\n\n## Multi-tenancy & privacy\n\n- Your Vapi key is read per-request and **never stored**. It travels TLS-encrypted to this val and on to Vapi, like any BYOK MCP server.\n- Call state, transcripts, and reports **are stored** in this val's SQLite, scoped by a `tenant` = first 12 hex chars of SHA-256(your key) — unguessable without the key, and it's how Vapi's webhooks find their way back (`/webhook/`, verified via `x-vapi-secret`).\n- Want full custody? **Remix this val** — it needs zero configuration.\n\n## Notes\n\n- The agent ends calls itself via Vapi's `endCall` tool once the objective is met; `max_minutes` (default 10) is a hard cap. 30s of mutual silence also ends it.\n- Latency is set by Vapi's STT→LLM→TTS pipeline (~1-2s/turn), not your agent's reasoning loop — that's the whole point vs. the puppet.\n- Post-call analysis (summary + structured data) completes a few seconds *after* the call ends, so `await_report` may return `done:false` once right at hangup — just call it again.\n- Free Vapi numbers are test-grade (occasional dead bridges, a daily outbound limit). Import a Twilio number into Vapi for reliability.\n- Only call numbers you have consent to call — automated calls without consent violate TCPA/FCC rules.\n",[190],"pulls","branches","forks","envVars",{"_203":205,"_206":64},"connections",{"_203":64,"_204":64},"count","unfilledCount",4,"emptyCount",{"_43":189,"_32":190,"_30":191,"_153":-5,"_41":42,"_40":192,"_154":25,"_38":229,"_13":64,"_33":20,"_35":194,"_24":25,"_157":-5,"_214":230,"_27":231},{"_43":182,"_32":183,"_30":184,"_153":-5,"_41":42,"_40":185,"_154":25,"_38":226,"_13":172,"_33":34,"_35":187,"_24":25,"_157":-5,"_214":227,"_27":228},{"_43":175,"_32":176,"_30":58,"_153":-5,"_41":42,"_40":177,"_154":25,"_38":223,"_13":164,"_33":179,"_35":180,"_24":25,"_157":-5,"_214":224,"_27":225},{"_43":167,"_32":168,"_30":169,"_153":-5,"_41":42,"_40":170,"_154":25,"_38":220,"_13":172,"_33":34,"_35":173,"_24":25,"_157":-5,"_214":221,"_27":222},{"_43":159,"_32":160,"_30":161,"_153":-5,"_41":42,"_40":162,"_154":25,"_38":217,"_13":164,"_33":34,"_35":165,"_24":25,"_157":-5,"_214":218,"_27":219},{"_43":152,"_32":28,"_30":31,"_153":-5,"_41":42,"_40":14,"_154":25,"_38":213,"_13":156,"_33":34,"_35":37,"_24":25,"_157":-5,"_214":215,"_27":216},["D",1781490036535],"children",[],[28],["D",1781490023255],[],[160],["D",1781486990915],[],[168],["D",1781490000709],[],[176],["D",1781486839919],[],[183],["D",1781486817181],[],[190],["D",1781486817181],["D",1789509672838],{"_104":103,"_112":108},{"_104":103,"_105":106,"_107":108,"_18":56,"_109":110,"_111":25},{"_13":90,"_32":91,"_43":42,"_92":44,"_93":94,"_95":96,"_97":50,"_98":99,"_100":66,"_101":45,"_102":103},"ctx",{"_83":-5,"_134":270,"_271":272,"_273":274,"_275":25,"_276":25,"_277":25,"_278":60,"_279":-5,"_280":-7,"_281":-7,"_282":64,"_283":25,"_284":25,"_285":-5,"_286":25,"_287":25,"_288":25,"_289":-5},"ENV",{"_241":242,"_243":244,"_245":91,"_246":247,"_248":249,"_250":251,"_252":253,"_254":255,"_256":257,"_258":259,"_260":261,"_262":263,"_264":265,"_266":267,"_268":269},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","e87eb04e707cf64e7e532c8e8ec395f209e17f15","RENDER_GIT_BRANCH","POSTHOG_API_TOKEN","phc_nmoyuAkStEZuUp69rYlg1DZgrIt19Gx0Y4Hwf8tGgJP","POSTHOG_API_HOST","https://wart.val.town","WEB_API_TEMPLATE","https://{val}.web.val.run","APP_URL","https://www.val.town","MODULE_URL","https://esm.town","VANITY_WEB_API_TEMPLATE","https://{label}.val.run/","DENOLS_URL","https://lsp.val.town","NODE_ENV","production","CLOUDFLARE_TURNSTILE_SITE_KEY","0x4AAAAAACtaIZJH7s9rBMZ6","GOOGLE_CLIENT_ID","875989696899-jtsqkhlbdv5jrlsk8631p7ipjg3c6o62.apps.googleusercontent.com","STRIPE_PUBLISHABLE_KEY","pk_live_51N2zvWEpZTTLipIkVEwXrUG9AT0yeOOEYxszarZT1jpsZV58Ly6wlg3SltZpHN26CUalOBmUSY7adQRQiSkCZz2500qSXNkf8Y","API_URL","https://api.val.town",[],"preferencesKeybindings","default","preferencesTheme","light","preferencesFixers","preferencesCopilot","preferencesSelectionAi","preferencesCloseBrackets","preferencesChatPanelState","preferencesDprint","preferencesDenofmt","notificationCount","flagDenols","isBusinessOrgMember","businessOrgHandle","isBusinessOrgAdmin","isHigherTownieTier","navCollapsed","currentOrgId"]

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