mcp-tools

A Model Context Protocol server, implemented from the spec — no MCP SDK, no dependencies.

MCP is how a language-model client (Claude Desktop, an agent) discovers and calls tools a server exposes. It's JSON-RPC 2.0; a local server speaks it over stdio. This repo implements that protocol directly — the whole surface a tool server needs is initialize → notifications/initialized → tools/list → tools/call — so the protocol is legible instead of hidden behind a library.

It exposes five tools, all safe by construction — three fully local and deterministic, two read-only lookups against public endpoints (no keys, no writes):

A real MCP session over stdio — no client, no key, no network. calc is handed __import__("os").system("rm -rf ~") and answers isError with the AST element it refused; the server stays up, and the next call flags the one sentence the source does not support. Run it yourself: ./demo/session.sh. Play it as a terminal session — the text is selectable.

Use it with Claude Desktop

Add this to claude_desktop_config.json (Settings → Developer → Edit Config):

{
  "mcpServers": {
    "mcp-tools": { "command": "python", "args": ["-m", "mcptools"] }
  }
}

Restart Claude Desktop and ask it to "search your notes for how rate limiting allows bursts", "use calc to work out 17 * 23 + 4", or — the useful one — paste some source material and ask it to draft an answer and then grade its own answer against those sources. It discovers the tools and calls them.

faithfulness 50% — 1 of 2 claim(s) not supported by the sources

Claims your sources do not support:
  • It was adopted by 80% of search engines in 2011.
    ↳ figure(s) not in sources: 2011, 80

Cut these, or cite a source that backs them.

That last tool is the point of the whole thing: it gives an agent a way to check its own work before it answers, without trusting another model's opinion about it. Point search at your own notes with "env": {"MCPTOOLS_CORPUS": "/path/to/notes.json"} (a { "id": "text", ... } file).

Related MCP server: calculator-mcp-server

Run it directly

pip install -e .
python -m mcptools        # serves on stdio; type/paste JSON-RPC, one message per line

# the handshake, by hand:
{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-06-18","capabilities":{}}}
{"jsonrpc":"2.0","id":2,"method":"tools/call","params":{"name":"calc","arguments":{"expression":"2 + 3 * 4"}}}
# → {"jsonrpc":"2.0","id":2,"result":{"content":[{"type":"text","text":"14"}],"isError":false}}

The part worth stealing: it's testable without a client

An MCP server you can only exercise with Claude Desktop open isn't really testable. Because the protocol is plain JSON-RPC, the dispatch is a pure function of a message — so the suite drives the real handshake directly and launches the server in a subprocess and speaks MCP to it over stdio, asserting that three requests get three replies and the notification gets none. The guardrail is tested through the protocol too: code thrown at calc comes back as an MCP tool-error (isError: true), so the model sees the failure and the server stays up.

pip install -e ".[dev]" && pytest -q     # 39 tests, stdlib only

The two live tools are tested against fixtures, never the network: the fetcher is resolved at call time so a test can substitute it, and the suite passes with sockets blocked. What is tested for real is failure — a network problem comes back as an MCP tool error the model can read and route around, not an exception that takes the server down for every other tool.

Operating it: logs, metrics, and an optional trail

A server you deploy needs more than correct replies — and all three of these are stdlib, none of them touch stdout (that's the JSON-RPC channel; a stray write corrupts the protocol):

• Structured logs on stderr. Every tools/call emits one JSON line — tool, duration_ms, isError, and the size of each argument — with a serving and a stopping line bracketing the session, the latter carrying a per-tool usage summary. See obs.py.

• In-process metrics. Calls and errors per tool, as a snapshot and on shutdown.

• An optional result trail. Point MCPTOOLS_DB at a file and the results of grade_answer and model_drift are persisted to SQLite — one row per call, behind a hand-rolled migration keyed on PRAGMA user_version (the zero-dependency form of Alembic). Unset, nothing is written and the server behaves exactly as before. See store.py.

Run it in Docker

docker build -t mcp-tools .
docker run -i --rm mcp-tools           # MCP over stdio; -i keeps stdin open
# persist the trail — mount a dir and point MCPTOOLS_DB at it:
docker run -i --rm -v "$PWD/data:/data" -e MCPTOOLS_DB=/data/history.db mcp-tools

CI builds this image and completes a real MCP handshake through it, so "it runs in a container" is checked, not claimed.

Design notes

• Notifications get no reply. A JSON-RPC message with no id is a notification; notifications/initialized is handled by producing nothing, per the spec.

• Two error channels, on purpose. An unknown method or a missing argument is a JSON-RPC protocol error (-32601 / -32602); a tool that fails returns a result with isError: true. The model should adapt to a failed tool call, not have the connection torn down under it.

• Why from scratch. The official SDK is excellent and the right choice for production. Implementing the protocol directly here is the point of the repo: ~150 lines makes the whole lifecycle visible, and it keeps the dependency count at zero.

MIT · by Erik Hill