Toolsmith

An MCP server that rewrites its own tool surface from usage data, and measures whether that made it better.

The claim, and why it needs a benchmark

"Self-improving" is a phrase that usually means nothing. Something changes, the output looks different, and nobody can say whether it improved. This repository is organized to make that failure impossible: the benchmark is built before the optimizer, results are reported on tasks never used for optimization, and every proposed change is kept only if it survives measurement.

The headline number is tool-selection accuracy on a held-out task set, before and after optimization. If that number does not move, the project's finding is that this approach does not work — which is a result, and it gets reported as one.

What the server does

A tool surface needs to be real to be worth optimizing, so toolsmith serves codebase intelligence over a Git repository:

Tool	Purpose
search_code	Content search with path and language filters
read_file	File contents, optionally a line range
find_symbol	Locate a definition by name
find_references	Where a symbol is used
trace_dependencies	Import and call graph traversal
search_commits	Commit history by message, author, or path
blame_range	Who last changed a line range, and in which commit

Seven tools with genuinely overlapping purposes — search_code, find_symbol, and find_references are all "find where a thing is", which is exactly the ambiguity that makes tool selection hard and therefore worth optimizing.

The three optimizations

Description rewriting. Telemetry records every call: which tool, what arguments, whether it succeeded, and how it failed. A tool that is frequently selected for tasks it cannot serve has a description that overclaims; a tool that is passed over for tasks it handles well has one that underclaims. Both are rewritten, re-benchmarked, and kept only on improvement.

Schema tightening. Parameters that models consistently malform — free-text where an enum belongs, an ambiguous date format, a path that should be repo-relative — get constrained so the mistake becomes unrepresentable rather than merely discouraged.

Macro synthesis. When telemetry shows a call chain recurring — find_symbol → read_file → find_references, over and over — a single composite tool is proposed for it. Fewer round trips, less context consumed, fewer chances to pick wrong.

What this deliberately does not do

It does not generate or register executable tool code. A server that writes new code for itself and then runs it is a much more interesting demo and a genuinely worse idea: arbitrary generation plus execution, in the component an agent is trusting. Descriptions, schemas, and composition of existing implementations get the result without that.

Measuring

bench/ holds a task suite split into a development half and a held-out half. Optimization only ever sees development tasks. Reported numbers only ever come from the held-out half. Overfitting a benchmark and calling it self-improvement is the failure mode this project exists to avoid.

Stack

Python 3.12, the official MCP Python SDK, claude-opus-5 with adaptive thinking for rewriting and for benchmark scoring.

Install

uv sync
uv run pytest

Serving the tools needs only a Git repository to point at. Optimization and benchmarking need ANTHROPIC_API_KEY.

Status

Under active development. See PLAN.md for the milestone breakdown and PROGRESS.md for the running log.

License

MIT