iflow-mcp_beverm2391-chain-of-thought-mcp

# Chain of Thought MCP Server Anthropic's recent article ["The "think" tool: Enabling Claude to stop and think in complex tool use situations"](https://www.anthropic.com/engineering/claude-think-tool) shows that using an external `think` tool notably increases performance on [SWE Bench](https://www.swebench.com/). This MCP Server uses Groq's API to call LLMs which expose raw chain-of-thought tokens from Qwen's [qwq model](https://qwenlm.github.io/blog/qwq-32b/). ## Installation 1. Clone this repository to your local machine. 2. Run ```uv sync``` to install depencies 3. Get a Groq API key from [here](https://console.groq.com/keys). 4. Update your mcp configuration with: ```json "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" } } } ``` The path should be the local path to this repository. You can get this easily by running `pwd` in the terminal from the root of the repository. ## Instructing The AI To Use This MCP Server I personally prefer the agent call this tool on every request to increase performance. I add this to my rules for the agent: ```xml <IMPORTANT> <when_to_use_tool> You should call the mcp chain_of_thought tool every time you talk to the user, which generates a chain-of-thought stream which you will use to complete the user's request. </when_to_use_tool> Before taking any action or responding to the user use the chain of thought tool as a scratchpad to: - List the specific rules that apply to the current request - Check if all required information is collected - Verify that the planned action complies with all policies - Iterate over tool results for correctness Here are some examples of what to iterate over inside the think tool: <cot_tool_example_1> User wants to cancel flight ABC123 - Need to verify: user ID, reservation ID, reason - Check…