What dependencies are required to run the server?
Node and NPM are required (developed on Node 22.14.0). MongoDB must be available, either via Docker (`docker-compose up`) or a custom instance with matching environment variables.
@Byskov-Soft
基本信息
MCP server providing controlled CRUD access to a database
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"data_service": {
"command": "/<path>/run.sh",
"args": []
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
The AI Autonomous Data Manager MCP is a specialized data management system that gives AI agents (in editors like Cursor, Cline) autonomous control over dynamically structured data collections. It allows AI assistants to maintain persistent memory across conversations, organize information, and manage data without human intervention.
Install Node and NPM (developed on Node 22.14.0) and run npm install. Start MongoDB (e.g., via docker-compose up). For STDIO mode, configure your MCP client (e.g., Cursor's mcp.json) to invoke the run.sh script. For SSE mode, run npm start and point the client to http://localhost:3001/sse. Use scripts like start, dev, or start:prod for various development and production scenarios.
Node and NPM are required (developed on Node 22.14.0). MongoDB must be available, either via Docker (`docker-compose up`) or a custom instance with matching environment variables.
Data is stored in MongoDB. The server maintains persistent data that survives across chat sessions, allowing AI agents to retain information.
The server supports both STDIO mode (for local MCP clients) and SSE (Server-Sent Events) mode (for remote access, including the built-in web interface).
SSE mode is described as "sketchy at times"; it works reliably with the MCP Inspector but has occasionally crashed when used with Cursor or Cline as the client. The server was created as an exercise and is provided as-is under the MIT license.
When running in SSE mode, collections can be viewed through the built-in web interface at http://localhost:3001. Alternatively, the MCP Inspector tool or the server's API endpoints can be used. Collections can also be exported to PDF via the web interface.

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