Token Optimization Summary for modelscope-mcp

Changes Made

Optimized tool descriptions in both server.js and deno-server.js to explicitly guide AI agents toward minimal token usage.

1. execute_command Tool

Before:

"Execute a shell command inside the container and return output."

After:

"Run commands with minimal output. Use sparingly and inspect only what is needed. Prefer precise paths, targeted filters (grep, grep -m, sed -n), and bounded output (head, tail, find with limits). Combine related checks concisely. Never dump entire large files, directories, logs, dependency trees, or recursive listings unless explicitly necessary. Avoid verbose flags and redundant output."

Impact: Strongly encourages AI to:

  • Use the tool sparingly
  • Request bounded output at the source
  • Avoid large dumps (files, directories, recursive listings)
  • Combine related checks
  • Use targeted filters (grep -m, sed -n, head, tail, find with limits)

2. read_file Tool

Before:

"Read an inclusive line range from a UTF-8 text file.

Line numbers are 1-based. If end_line is omitted, read through EOF."

After:

"Read only the smallest useful line range. Lines are 1-based. Prefer bounded start_line/end_line ranges and avoid reading to EOF for large files unless necessary. If the relevant location is unknown, locate it first with a targeted search, then read only the surrounding lines."

Impact: Strongly encourages AI to:

  • Read minimal line ranges
  • Use explicit start_line and end_line
  • Avoid reading to EOF for large files
  • Locate sections first with targeted searches, then read only relevant lines

3. write_file Tool

No changes - This tool is for output, not inspection, so token optimization guidance is not applicable.

Implementation Details

Server-Side Response Format

  • Preserved the existing compact behavior: return result.result ?? ""
  • No verbose metadata, command echoes, status labels, or wrappers added
  • Clean direct output only

No Automatic Truncation

  • Did NOT add server-side output truncation
  • Rationale: Blind truncation can silently remove important information
  • Instead, the descriptions guide AI to request bounded output at the command level

Files Modified

  • server.js (Node.js implementation)
  • deno-server.js (Deno/Val Town implementation)

Verification

  • ✅ Syntax validated with Node.js
  • ✅ Tool names unchanged
  • ✅ Input schemas unchanged
  • ✅ MCP protocol compatibility preserved
  • ✅ Stateless HTTP behavior unchanged

Expected Behavior

AI agents using this MCP server should now:

  1. Request fewer, more targeted inspections
  2. Use command-line tools to limit output (head, tail, grep -m, etc.)
  3. Read specific line ranges instead of entire files
  4. Locate relevant sections before reading
  5. Avoid expensive operations like recursive listings and dependency tree dumps

Token Savings Estimate

Conservative estimate for typical AI agent sessions:

  • Before: 10,000-50,000 tokens per debugging/inspection session (large file dumps, recursive listings)
  • After: 2,000-10,000 tokens per session (targeted inspections, bounded output)
  • Reduction: 60-80% in inspection-heavy workflows

Next Steps

  1. Review these changes
  2. Test with a live AI agent to measure actual token reduction
  3. Deploy to Val Town when ready (no push/deployment performed per instructions)