Chain of Thought

Provides a chain-of-thought enabled MCP server via Groq API to generate tokenized thinking streams for user requests.

python

11

GitHub Stars

python

Language

6 months ago

First Indexed

2 months ago

Catalog Refreshed

Documentation & install

Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs

{
  "mcpServers": {
    "beverm2391-chain-of-thought-mcp-server": {
      "command": "uv",
      "args": [
        "--directory",
        "path/to/cot-mcp-server",
        "run",
        "src/server.py"
      ],
      "env": {
        "GROQ_API_KEY": "YOUR_GROQ_API_KEY"
      }
    }
  }
}

You can run an MCP server that exposes a chain-of-thought capable processing flow, enabling a dedicated reasoning scratchpad for the AI during user interactions. This server uses Groq's API to access LLMs that provide chain-of-thought tokens, which helps you reason through tasks step by step and coordinate complex tool use. It is useful for scenarios where you want structured reasoning and explicit intermediate steps to inform decisions.

How to use

To use this MCP server with an MCP client, you configure an MCP connection that starts the server process and provides the necessary API key. Once configured, your client can invoke this MCP to obtain a chain-of-thought aware response stream that you can incorporate into your assistant’s reasoning workflow.

General usage pattern once the server is running:

Start the MCP using the provided command.
Ensure your client passes an environment variable or configuration entry with the Groq API key.
Invoke the MCP by name (chain_of_thought) from your client, passing your user request as the input payload.
Consume the returned chain-of-thought stream and the final answer as part of your assistant’s reply. Use the chain-of-thought notes as a scratchpad to verify rules, required information, and policy constraints before completing actions.

How to install

Prerequisites you need before installation: you should have Python available on your system and the uv tool installed to run MCP server commands.

Step-by-step installation and setup:

Clone this repository to your local machine.
Run uv sync to install dependencies.
Obtain a Groq API key from the provider and keep it handy.
Update your MCP configuration with the following snippet, replacing the path with the local repository path on your machine and injecting your Groq API key where shown.

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