"Importing FastMCP from the mcp.server Python Module" matching MCP servers:

A typed FastMCP server that wraps Tavily's web research API, providing tools for search, extract, map, crawl, and research workflows via MCP with prompts and resources.

A temporally-aware knowledge graph MCP server for AI agents, enabling episodic ingestion, entity management, and semantic search with support for multiple LLM and embedding providers.

Enables secure, high-concurrency AI integrations through MCP, providing semantic document search, SSRF-protected external context fetching, and cached user profile retrieval.

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.

FastMCP is a comprehensive MCP server allowing secure and standardized data and functionality exposure to LLM applications, offering resources, tools, and prompt management for efficient LLM interactions.

A Model Context Protocol server that bridges MCP clients with local LLM services, enabling seamless integration with MCP-compatible applications through standard tools like chat completion, model listing, and health checks.

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

Provides a retrieval/MCP knowledge server for the Unreal Engine Python API, enabling AI agents to retrieve real signatures and documentation before writing code.

A Python implementation of the Model Context Protocol that enables applications to provide standardized context for LLMs, allowing developers to build servers that expose data and functionality to LLM applications.

Enables MCP clients to call document retrieval, safe arithmetic, Tavily web search, and OCR tools exposed by the repository, returning structured results for external agent workflows.

Exposes a FastMCP server that proxies to an existing LightRAG Server API, enabling querying, document insertion, and management of a LightRAG knowledge base through MCP tools.

An enterprise-ready MCP server that exposes a RAG tool for retrieving relevant context and metadata from a Qdrant vector database using natural language queries.

A modern Model Control Plane (MCP) project that provides a lightweight, extensible foundation for building and deploying intelligent systems that manage and expose AI/LLM capabilities through Python, FastAPI, and Docker.

A demonstration server that automatically generates Model Context Protocol (MCP) servers from OpenAPI specifications, providing seamless integration with FastAPI and supporting various transmission methods like STDIO, HTTP, and SSE.

Integrates R2R (Retrieval-Augmented Generation) with Claude Desktop, enabling semantic search across knowledge bases and RAG-based question answering with support for vector, graph, web, and document search.

Provides tools to fetch and search news from The Verge's RSS feed, allowing users to get today's news, retrieve random articles from the past week, and search for specific keywords in recent Verge content.

Enables semantic search over a Qdrant knowledge base through MCP, with optional domain filtering and stdio or Streamable HTTP transport.

A Python port of the official Perplexity MCP server that enables web-grounded Q\&A, deep research, and step-by-step reasoning via Perplexity AI's models. It supports both STDIO and HTTP interfaces for seamless integration with tools like Claude Desktop and Cursor.

A FastMCP-based MCP server for the R2R API, enabling integration with document management, knowledge graphs, and RAG systems through automatically generated tools and resources.

A production-grade MCP server and client implementation with comprehensive features including structured logging, health checks, metrics, authentication, and RAG capabilities with PostgreSQL vector search. Supports both stdio and SSE transports with containerization and security features for enterprise deployment.