"Using MySQL's MySQL Connector/Python (mcp) with Cursor" matching MCP servers:

A Model Context Protocol server that enables AI agents to query a Graphiti knowledge graph and pgvector document store for evidence-backed responses via hybrid search and RAG.

Stdio MCP server for Cortex, a patent-pending semantic memory for AI agents: seven tools (search, fetch, recall, save with quality gate, write status, conflicts, forget) forwarded to the hosted Cortex service, shared by ChatGPT, Claude and any MCP client.

An MCP server that provides read-only access to MySQL databases.

Enables interaction with MySQL databases (including AWS RDS and cloud instances) through natural language. Supports database connections, query execution, schema inspection, and comprehensive database management operations.

Enables web search, content extraction, question answering, and RAG context generation using Tavily API with Cursor-compatible underscore-named tools.

Enables LLMs to query and analyze data across multiple MySQL databases with enterprise data federation, intelligent caching, cross-system analytics, and automated schema discovery for comprehensive business intelligence.

Enables agents to pull compact MySQL row diffs into their context, supporting before/after changes via triggers or watermark-based updates, with tools to fetch and acknowledge changes.

Enables Claude to interact with a MySQL database by automatically discovering its schema and retrieving full table data for analysis.

A Model Context Protocol server that enables SQL query execution, database management, and business intelligence capabilities through MySQL connections.

A server that enables AI models to interact with MySQL databases through a Model Control Protocol, providing tools for table creation, schema inspection, query execution, and data retrieval.

Enables natural language database operations on MySQL databases with AI integration, supporting CRUD operations, schema inspection, and audit logging with built-in security features including SQL injection protection and permission controls.

MCP server providing filesystem allowlist and pack-aware Qdrant RAG search. Enables reading files, listing directories, and performing filtered RAG searches with case and pack IDs.

A MCP server to allow the LLM in Cursor to access Rust Analyzer, Crate Docs and Cargo Commands.

Enables searching through vectorized Cursor IDE chat history via a FastAPI service powered by LanceDB and Ollama.

A Python MCP server providing AI agents with tools for local knowledge base search, business context retrieval, and text summarization prompt generation.

Unified AI Agent SaaS Connector & Multi-Provider Gateway connecting to 1,000+ SaaS platforms, vector databases, and 500+ LLM models via MCP, REST API, and CLI.

A TypeScript MCP server that provides secure, read-only access to a locally hosted MySQL database for AI-agent workflows, with configurable policies and support for multiple MCP clients.

Connects AI clients to MindsDB via the MySQL protocol to execute SQL queries, manage databases, and perform semantic searches within knowledge bases. It enables automated workflows through job scheduling and provides seamless integration with external data sources.

A universal interface that enables AI Agents to seamlessly communicate with Adb MySQL databases, allowing them to retrieve database metadata and execute SQL operations.

Enables cost-effective repository analysis, code search, file editing, and task planning by wrapping the cursor-agent CLI through focused tools. Reduces token usage by offloading heavy thinking tasks from Claude to specialized operations with configurable output formats.

Enables exposing third-party data sources as Model Context Protocol servers with shared HTTP, rate limits, retries, caching, validation, and observability through thin per-source adapters.

A Model Context Protocol (MCP) server that converts natural language queries into SQL statements, allowing users to query MySQL databases using conversational language instead of writing SQL code.

Provides retrieval-augmented generation (RAG) capabilities by ingesting various document formats into a persistent ChromaDB vector store. It enables semantic search and retrieval using either OpenAI or Ollama embeddings for processing local files, directories, and URLs.

Turns a user's own Markdown wiki into a locally indexed knowledge base, enabling semantic search, per-page retrieval, source listing, and citation-backed RAG question answering. All embedding and generation run through a local Ollama instance, so queries and documents never leave the machine.

Enables natural language querying of Dynamics 365 Finance & Operations by using Azure OpenAI to generate OData queries, fetch data, and refine responses.

Implements a Model Control Protocol server integrated with Google Gemini LLM, providing a flexible framework for building AI-powered applications.

A Model Context Protocol server that enables natural language querying of MySQL databases, supporting both HTTP-based communication and MCP stdio protocol.

Enables semantic search over your Cursor IDE chat history by vectorizing prompts and storing them in LanceDB. Provides a Dockerized API to perform vector similarity searches against your chat history.

Exposes search over internal documents and company lookup via authenticated API, enabling LLMs to access firm files and data.

Enables natural language search and analysis of uploaded PDF, CSV, and Excel documents using retrieval-augmented generation and MCP tools, providing contextual answers to user queries.