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think-mcp-server

by marcopesani

A minimal MCP Server that provides Claude AI models with the 'think' tool capability. It enables Claude to perform better on complex reasoning tasks involving multi-step tool usage.

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What is think-mcp-server?

This server implements a minimal Message Control Protocol (MCP) server that provides Claude AI models with the 'think' tool capability, allowing Claude to pause during response generation to consider whether it has all necessary information to proceed.

How to use think-mcp-server?

Install the server via npm or Docker, configure your AI client (Claude Desktop, Cline, Cursor) to use the server, and include instructions for using the 'think' tool in your prompts, ideally with examples in the system prompt.

Key features of think-mcp-server

  • Enables Claude to think about a problem or analyze information using the 'think' tool.

  • Improves complex problem-solving performance.

  • Enhances policy adherence in tool usage.

  • Increases consistency in decision making.

Use cases of think-mcp-server

  • Tool Output Analysis: When Claude needs to process previous tool call outputs before acting.

  • Policy-Heavy Environments: When Claude must follow detailed guidelines.

  • Sequential Decision Making: When each action builds on previous ones and mistakes are costly.

  • TypeScript Development: Analyzing type definitions, checking for type errors, reasoning through generics.

FAQ from think-mcp-server

What is the 'think' tool?

The 'think' tool gives Claude the ability to include an additional thinking step—with its own designated space—as part of reaching a final answer.

How does the server communicate with Claude?

The server operates using the Model Context Protocol (MCP) to communicate with Claude and similar AI assistants using stdio.

What input does the 'think' tool require?

The 'think' tool requires a 'thought' (string) containing Claude's thinking process.

Where should I include instructions for using the 'think' tool?

For best results, include clear instructions in your prompts and add examples of effective thinking patterns in your system prompt.

What are the performance benefits of using the 'think' tool?

Anthropic's evaluations showed significant improvements, including a 54% relative improvement on τ-Bench airline domain and improved performance on SWE-bench by 1.6% on average.