How is MongoDB MCP Server configured?
Configuration is managed through a `config.yml` file with MongoDB connection details (host, port, user, password, database) and MCP server settings (name, version, base URL, address, SSE toggle).
@thinking-bzf
基本信息
A Model Context Protocol (MCP) server that enables LLMs to connect MongoDB using mcp-go.
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。
A Model Context Protocol (MCP) server that enables LLMs to connect MongoDB using mcp-go. It acts as a bridge between the LLM and the MongoDB database, allowing you to perform CRUD operations using natural language.
Copy the config.yml.example file to config.yml, modify it with your MongoDB and MCP server settings, then run the server using go run main.go. The server exposes tools for querying and managing collections, documents, and indexes.
Configuration is managed through a `config.yml` file with MongoDB connection details (host, port, user, password, database) and MCP server settings (name, version, base URL, address, SSE toggle).
It provides tools for find, count, listCollections, insertOne, updateOne, deleteOne, createIndex, dropIndex, and indexes.
Yes, SSE support is enabled by default (configurable via the `sse` field in `config.yml`).
The server is written in Go and is started with `go run main.go`. It uses the mcp-go library and the official MongoDB Go driver.
Yes, the README shows usage in DeepChat with a screenshot depicting integration.
MCP server for interacting with Neon Management API and databases

Connect Karma.Domains to ChatGPT, Claude, Cursor, VS Code, or another MCP client — and find a domain in plain language. The same auctions, expired, backorder, and buy-now data you use in the app, plus saved filters, note
A powerful multi-database server implementing the Model Context Protocol (MCP) to provide AI assistants with structured access to databases.

Model Context Protocol Servers
Drop-in Apache Spark replacement written in Rust, unifying batch processing, stream processing, and compute-intensive AI workloads.
评论