Chain of Thought MCP Server

Name: Chain of Thought MCP Server
Author: beverm2391

@beverm2391

Repository

About Chain of Thought MCP Server

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

Basic information

Config

Add this server to your MCP-compatible client using the configuration below.

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

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

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.