MCP Think Tool Server

A Model Context Protocol (MCP) server that implements the "think" tool for enhancing complex reasoning capabilities in Large Language Models (LLMs). This tool provides LLMs with a dedicated space for structured thinking during problem-solving tasks, significantly improving performance in complex scenarios requiring policy adherence and multi-step reasoning.

🧠 Overview

The Think Tool MCP server is based on Anthropic's research demonstrating that providing LLMs with a dedicated "thinking space" dramatically improves performance on complex tasks. This tool allows any compatible LLM (Claude, GPT-4, and others) to:

• Break down complex problems into manageable steps
• Perform structured reasoning and analysis
• Verify policy compliance during decision-making
• Process and synthesize information from multiple tool calls
• Maintain context and logical flow in long reasoning chains

As described in Anthropic's blog post, the think tool has shown significant improvements in tasks requiring complex reasoning and policy adherence across different language models.

✨ Features

• 🔧 Structured Thinking Space: Provides LLMs with a dedicated environment for complex reasoning
• 📝 Memory Aid: Helps maintain context during long chains of tool calls
• 🎯 Policy Verification: Enables careful policy adherence checking
• 🔍 Problem Decomposition: Supports breaking down complex problems into steps
• ⚡ Lightweight: Minimal overhead with efficient MCP implementation
• 🔌 Easy Integration: Simple setup with popular AI platforms (Cursor, Claude Desktop, etc.)
• 🛠️ TypeScript: Built with TypeScript for type safety and better development experience
• 🌐 Universal Compatibility: Works with any LLM that supports the Model Context Protocol

🚀 Platform Configuration

Cursor IDE

Requirements: Cursor version 0.45.6 or higher

1. Open Cursor Settings (Cmd/Ctrl + ,)

2. Navigate to Features → MCP Servers

3. Click "+ Add New MCP Server"

4. Configure the server:

• Name: think-tool-mcp (or your preferred name)
• Type: command
• Command: npx -y think-tool-mcp

5. Save and restart Cursor

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "think-tool": {
      "command": "npx",
      "args": ["-y", "think-tool-mcp"]
    }
  }
}

Config file locations:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

Other MCP-Compatible Platforms

This server works with any platform supporting the Model Context Protocol. Refer to your platform's documentation for MCP server configuration.

📊 Performance Analysis

Extensive research by Anthropic has demonstrated significant performance improvements when LLMs use the think tool. The following results showcase the measurable impact across different benchmarks and use cases.

τ-Bench (Tau-Bench) Results

τ-Bench is a comprehensive benchmark designed to test LLM tool usage in realistic customer service scenarios. It evaluates the ability to navigate complex conversations, follow detailed policy guidelines, and maintain consistency across multiple task trials.

Airline Domain Performance

The airline domain represents a complex policy-heavy environment where precise adherence to detailed rules is critical.

Key Findings:

• 54% relative improvement in pass^1 metric (0.584 vs 0.370 baseline)
• Optimized prompting with examples dramatically enhanced performance
• Improvements maintained across all trial consistency levels (k=1 to k=5)

Retail Domain Performance

The retail domain has simpler policies, allowing the think tool to show benefits even without extensive prompting.

Key Findings:

• 3.7% improvement in pass^1 metric without additional prompting
• Demonstrates effectiveness across varying complexity levels
• Consistent performance gains maintained across multiple trials

SWE-Bench Results

SWE-Bench evaluates coding performance on real-world software engineering tasks. The think tool contributed to Claude 3.7 Sonnet achieving state-of-the-art performance.

Performance Impact:

• Baseline Score: 62.3% (without think tool)
• With Think Tool: 64.9% (estimated based on 1.6% improvement)
• Statistical Significance: Welch's t-test: t(38.89) = 6.71, p

Enhancing AI reasoning, one thought at a time.