Chatsum MCP Server

What does “Chatsum” MCP Server do?

The Chatsum MCP (Model Context Protocol) Server is designed to enable AI assistants to query and summarize chat messages from a user’s chat database. By acting as a bridge between AI agents and stored chat histories, Chatsum MCP Server enhances development workflows by allowing large language models (LLMs) to efficiently retrieve and condense relevant chat data. This makes it possible for developers and end-users to surface insights, track conversations, or gather summaries from vast message logs directly within their preferred AI tools or platforms. The server facilitates tasks such as querying specific messages based on parameters and generating concise summaries, thereby streamlining the process of managing and understanding chat data.

List of Prompts

No prompt templates are mentioned in the available repository documentation.

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List of Resources

No explicit MCP “resources” are described in the available documentation or code.

List of Tools

• query_chat_messages
  Query chat messages with given parameters and summarize chat messages based on the query prompt.

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Use Cases of this MCP Server

• Chat History Summarization: Developers can leverage the server to quickly summarize lengthy chat histories, enabling faster review and extraction of important points.
• Conversation Search and Analytics: Efficiently search through chat logs to find specific messages, topics, or patterns, aiding in analytics or customer support scenarios.
• AI-Powered Chat Insights: Use LLMs to generate actionable insights or highlights from chat data, improving productivity for teams and individuals.
• Integration with Personal Assistants: Enhance personal or team AI assistants by giving them the ability to reference and summarize past conversations for context-aware responses.

How to set it up

Windsurf

No setup instructions found for Windsurf.

Claude

1. Ensure you have Node.js and pnpm installed.
2. Set up your chat database by following instructions in the chatbot directory.
3. Edit the Claude config file:
   • MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json
   • Windows: %APPDATA%/Claude/claude_desktop_config.json
4. Add the following MCP server configuration:

   {
     "mcpServers": {
       "mcp-server-chatsum": {
         "command": "path-to/bin/node",
         "args": ["path-to/mcp-server-chatsum/build/index.js"],
         "env": {
           "CHAT_DB_PATH": "path-to/mcp-server-chatsum/chatbot/data/chat.db"
         }
       }
     }
   }
   

5. Save the file and restart Claude to verify the server is running.

Securing API Keys

Set secrets like database paths using the env field in your JSON config:

"env": {
  "CHAT_DB_PATH": "path-to/mcp-server-chatsum/chatbot/data/chat.db"
}

Cursor

No setup instructions found for Cursor.

Cline

No setup instructions found for Cline.

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:

{
  "chatsum": {
    "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 “chatsum” to whatever the actual name of your MCP server is (e.g., “github-mcp”, “weather-api”, etc.) and replace the URL with your own MCP server URL.

Overview

Based on the available information, the Chatsum MCP Server provides a specific, well-implemented tool for chat querying and summarization, but lacks documentation on prompt templates, MCP resources, and broader platform setup support. This makes it a focused but somewhat limited MCP server for general workflows.

MCP Score

Rating: 5/10
The Chatsum MCP Server offers a clearly defined tool for chat summarization and querying with good adoption (stars/forks), but lacks thorough documentation, resource exposure, and broader prompt/template support, limiting its versatility in MCP contexts.