mcp-server-chatsum is an MCP Server designed to summarize your chat messages. It queries chat messages based on given parameters and provides summarized outputs, making it useful for managing large volumes of chat data.
The primary purpose of mcp-server-chatsum is to help users efficiently summarize their chat history. By querying and processing chat messages, it allows for better organization and understanding of conversations, especially in environments with high message volume.
The author of mcp-server-chatsum is idoubi, who has also developed other tools such as chatbot-related resources.
mcp-server-chatsum can be integrated with applications like Claude Desktop by adding its server configuration to the respective app's settings file (e.g., `claude_desktop_config.json` on MacOS or Windows).
You should use mcp-server-chatsum when you need to manage and summarize extensive chat histories. It’s particularly helpful during development workflows or when working with databases containing significant amounts of conversational data.
To set up mcp-server-chatsum, follow these steps: move to the chatbot directory and configure the chat database as per the README instructions; create a `.env` file in the root directory and specify your chat database path using `CHAT_DB_PATH`; install dependencies via `pnpm install`, then build the server using `pnpm build`. For development purposes, use `pnpm watch` for auto-rebuild functionality.
Debugging mcp-server-chatsum can be done effectively using the MCP Inspector tool. Run `pnpm inspector` to generate a URL that provides access to debugging tools through your browser.
Users can find support and engage with others regarding mcp-server-chatsum through platforms like the MCP Server Telegram group or the MCP Server Discord channel.
The mcp-server-chatsum project includes essential files such as `README.md`, `package.json`, `pnpm-lock.yaml`, `.gitignore`, `tsconfig.json`, and others necessary for setting up and running the server.
MCP(Model Context Protocol,模型上下文协议)是一个开放协议,旨在标准化应用程序如何为大型语言模型(LLM)提供上下文信息。类似于 AI 应用的'USB-C 端口',MCP 确保 AI 模型能够与各种数据源和工具无缝连接。
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