"A MongoDB MCP that interacts with MongoDB servers" matching MCP servers:

Enables interaction with MongoDB databases through the Model Context Protocol, supporting CRUD operations, secure connections, and easy IDE integration.

Provides read-only access to legacy MongoDB instances (<4.0) via the Model Context Protocol, enabling AI models to query databases, collections, and run aggregations safely.

Enables interaction with MongoDB databases through complete CRUD operations including connecting, creating, reading, updating, and deleting documents with support for filtering, pagination, and automatic ObjectId serialization.

A MongoDB MCP Server that allows AI agents and MCP clients to interact with MongoDB databases through standardized tools for CRUD operations, schema discovery, and collection management.

An autonomous MCP server that enables LLMs to intelligently query and analyze MongoDB databases by reverse-engineering schemas, proving relationships, and enforcing security safeguards like PII masking and query limits.

A tool that enables automated browser control using Pyppeteer within the MCP framework, allowing navigation, screenshot capture, and element interaction with websites.

A powerful Model Context Protocol (MCP) server implementation that provides standardized interaction with MongoDB databases, supporting complete CRUD operations, async patterns, and real-time updates via SSE.

Enables querying and analyzing the MongoDB sample_mflix movie dataset through natural language, supporting searches by title, genre, actors, directors, ratings, and providing aggregate statistics like counts and averages.

Provides a standardized interface for AI assistants to interact with MongoDB databases, enabling CRUD operations, schema analysis, and database management through the Model Context Protocol.

Enables AI harnesses to maintain a persistent memory layer backed by a local SQLite file, providing MCP tools to add, search, deprecate, and synchronize facts without deleting history.

Enables basic banking operations such as account creation, deposits, withdrawals, and transfers using MongoDB for persistent storage.

Enables basic banking operations (CRUD) with persistent MongoDB storage, supporting account management and money transfers.

A learning MCP server that demonstrates tools and resources using FastMCP, with a greet tool and UI capabilities.

Enables MCP clients like Claude to query Chinese A-share market data, including K-line charts with forward/backward adjustment, limit-up and limit-down pools, per-stock fund flows, index quotes, and stock news, with automatic symbol normalization and retry handling for throttled data sources.

Provides A-share (Chinese stock market) quantitative analysis through tools for stock screening, northbound capital flow tracking, dragon-tiger list analysis, margin trading, sector analysis, technical indicators, IPO info, and limit-up/down statistics using akshare data.

Provides seamless access to the Protein Data Bank in Europe (PDBe) API and search capabilities, enabling AI clients to query protein structures, perform advanced searches, and retrieve structural biology data.

A workspace of six MCP servers—grounded-cv (hybrid RAG with citations), repo-intel (GitHub repo intelligence), web-research (safe web search/extraction), ats-jobs (unified ATS job boards), outcome-ledger (durable task ledger), and coding-workflows (code review & planning)—purpose-built for agentic hiring evaluations.

Provides a framework for running multiple MCP servers using FastAPI, stdio, and streamable-http transports, with an echo tool for testing and integration with MCP Inspector.

A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.

Enables AI coding agents to request a second opinion from a different frontier model via a single tool call, with PII scrubbed before transit, live web search, and support for Claude, ChatGPT, Cursor, and Gemini.