Think MCP Server

What does “Think” MCP Server do?

Think MCP is an implementation of an MCP (Model Context Protocol) server that provides a “think” tool for structured reasoning in agentic AI workflows. Inspired by Anthropic’s engineering research, this server enables AI assistants to pause and explicitly record their thoughts during complex tool use or multi-step reasoning. By integrating the “think” tool, agents can analyze tool outputs, backtrack decisions, comply with detailed policies, and improve sequential decision-making. Think MCP is designed to enhance AI development workflows by exposing explicit reasoning steps, making agent behavior more transparent and auditable. The server is minimal, standards-based, and ready for integration with Claude or other agentic large language models.

List of Prompts

• No explicit prompt templates are mentioned in the repository or documentation.

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List of Resources

• No specific resources (as defined by MCP) are listed or exposed by the Think MCP server.

List of Tools

• think: Allows the AI agent to append a thought to the log for structured reasoning. Input: thought (string).
• criticize (advanced mode): Additional tool for agents to critique or reflect on actions or decisions.
• plan (advanced mode): Enables the agent to outline a plan or sequence of steps.
• search (advanced mode): Lets the agent perform search operations, likely leveraging external APIs (requires TAVILY_API_KEY).

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Use Cases of this MCP Server

• Tool Output Analysis: Enables the AI to process and reflect on the results of previous tool calls, supporting robust agent reasoning.
• Policy Compliance: Supports agents working in policy-heavy environments by allowing them to explicitly verify compliance with guidelines at each step.
• Sequential Decision Making: Facilitates step-by-step planning and reasoning, where each action builds on prior context, improving multi-step workflows.
• Agent Self-Critique (Advanced Mode): Allows agents to critique and improve their own decisions, fostering self-improvement and error correction.
• External Search Integration (Advanced Mode): Empowers agents to search for additional information via APIs, broadening the context for more informed decisions.

How to set it up

Windsurf

1. Ensure you have Node.js and Windsurf installed.
2. Locate your Windsurf configuration file.
3. Add the Think MCP server to your mcpServers section:

   {
     "mcpServers": {
       "think-mcp": {
         "command": "uvx",
         "args": ["think-mcp"],
         "enabled": true
       }
     }
   }
   

4. Save the configuration and restart Windsurf.
5. Verify the setup by checking the MCP server is available in your agent.

Securing API Keys (Advanced Mode):

{
  "mcpServers": {
    "think-mcp": {
      "command": "uvx",
      "args": ["think-mcp", "--advanced"],
      "enabled": true,
      "env": {
        "TAVILY_API_KEY": "YOUR_TAVILY_API_KEY"
      }
    }
  }
}

Claude

1. Install and set up Claude with MCP server integration support.
2. Edit the configuration file to include Think MCP:

   {
     "mcpServers": {
       "think-mcp": {
         "command": "uvx",
         "args": ["think-mcp"],
         "enabled": true
       }
     }
   }
   

3. Save and restart Claude.
4. Confirm the MCP server is active within the Claude environment.

API Keys: Use the env section (see Windsurf example).

Cursor

1. Make sure Cursor supports MCP integration.
2. Open Cursor’s settings or configuration file.
3. Add Think MCP to the mcpServers object:

   {
     "mcpServers": {
       "think-mcp": {
         "command": "uvx",
         "args": ["think-mcp"],
         "enabled": true
       }
     }
   }
   

4. Save changes and restart Cursor.
5. Check for successful connection to the MCP server.

Cline

1. Install Cline and locate the config file.
2. Add the MCP server configuration:

   {
     "mcpServers": {
       "think-mcp": {
         "command": "uvx",
         "args": ["think-mcp"],
         "enabled": true
       }
     }
   }
   

3. Save and restart Cline.
4. Verify the server is running.

Securing API Keys: Use the env and inputs fields as shown above.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "think-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “think-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Based on these tables, the Think MCP server is minimal but focused: it implements the core “think” reasoning tool and adds a few advanced tools in enhanced mode. While it lacks prompt templates and resource exposure, its toolset is valuable for agentic reasoning. The README is clear and setup is straightforward. Rating: 6/10 — useful for research and prototyping, but not as feature-rich as some other MCP servers.

MCP Score