Chain of Thought MCP Server

Name: Chain of Thought MCP Server
Author: beverm2391

@beverm2391

リポジトリ

Chain of Thought MCP Server について

An mcp server to inject raw chain of thought tokens from a reasoning model.

基本情報

カテゴリ

その他

ランタイム

python

トランスポート

stdio

公開者

beverm2391

設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "chain_of_thought": {
      "command": "uv",
      "args": [
        "--directory",
        "path/to/cot-mcp-server",
        "run",
        "src/server.py"
      ],
      "env": {
        "GROQ_API_KEY": ""
      }
    }
  }
}

ツール

ツールは検出されませんでした

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is Chain of Thought MCP Server?

Chain of Thought MCP Server implements an external "think" tool that uses Groq’s API to call the Qwen qwq model, exposing raw chain-of-thought tokens to improve AI performance in complex tool-use situations. It is designed for developers who want to inject structured reasoning into their AI agents.

How to use Chain of Thought MCP Server?

Clone the repository, run uv sync to install dependencies, obtain a Groq API key, and add the server to your MCP configuration using the uv command with the source directory path and your API key. Instruct your AI (e.g., through agent rules) to call the chain_of_thought tool on every request as a scratchpad for planning and verification.

Key features of Chain of Thought MCP Server

Exposes raw chain-of-thought tokens from Qwen’s qwq model via Groq.
Boosts performance on complex tool‑use benchmarks like SWE‑Bench.
Acts as a scratchpad for rule verification and planning.
Integrates easily with any MCP‑compatible AI agent.
Simple installation with uv sync and one environment variable.

Use cases of Chain of Thought MCP Server

Automating multi‑step workflows (e.g., flight cancellations, bookings).
Validating compliance with business rules before taking action.
Iterating over tool results for correctness in sensitive operations.
Providing transparent reasoning paths for debugging AI decisions.
Enhancing Claude or other agents with structured thinking steps.

FAQ from Chain of Thought MCP Server

What makes this server different from a simple prompt?

It forces the agent to output a structured chain of thought in a separate tool call, which has been shown to improve performance on complex tasks by requiring explicit planning and verification before action.

What are the runtime dependencies?

Python, uv, a Groq API key, and a locally cloned copy of the repository.

Where does user data or reasoning live?

All processing occurs locally on your machine; the Groq API receives only the prompts you configure, and no persistent data storage is described.

Are there any known limits?

The README does not mention specific limits, but performance depends on Groq API rate limits and the qwq model’s context window.

How is transport and authentication handled?

Transport is STDIO (the MCP default), and authentication uses a Groq API key set via the GROQ_API_KEY environment variable.

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