mcp-tools
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In the chat, type
@followed by the MCP server name and your instructions, e.g., "@mcp-toolsCalculate 2 + 3 * 4"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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):
Tool | What it does | Why it's safe |
| Evaluate an arithmetic expression | Parses to an AST and allow-lists arithmetic nodes only — no |
| BM25 keyword search over a bundled corpus | Read-only, no network. The corpus is read once at startup; no tool argument can reach the filesystem. The ranking is Okapi BM25 — the same length-normalised, saturation-aware scoring that matches the published SciFact baseline in rag-eval-lab, reimplemented here so this server has zero dependencies. |
| Is a live model still scoring what it used to? | Read-only GET of the public model-drift board — accuracy, latency, answer length, reliability and refusal rate for 16 models, plus what moved since last week's run. No key, no write. |
| Did a project's latest eval run regress against the one before it? | Read-only GET of eval-history's per-case comparison — so a better average can't hide the case that broke. |
| Check a draft answer against its sources and name the sentences they don't support | No LLM judge. A model grading hallucination is itself a model output — you can't tell a real unsupported claim from the judge having an off day, and you can't reproduce last week's verdict. This is lexical: a figure that appears nowhere in the sources fails the sentence outright (invented statistics are the strongest tell), and low content-word coverage flags claims the sources never make. |
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).
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 # 31 tests, stdlib onlyThe 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.
Design notes
Notifications get no reply. A JSON-RPC message with no
idis a notification;notifications/initializedis 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 withisError: 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
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