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Open-Source MCP servers

Production-ready MCP servers that extend AI capabilities through file access, database connections, API integrations, and other contextual services.

11,782 servers. Last updated -

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    This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
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    Apache 2.0
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    A Machine Control Protocol (MCP) server that enables storing and retrieving information from a Qdrant vector database with semantic search capabilities.
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    Enables semantic search across multiple Qdrant vector database collections, supporting multi-query capability and providing semantically relevant document retrieval with configurable result counts.
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    MIT License
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    A Model Context Protocol server that enables semantic search capabilities by providing tools to manage Qdrant vector database collections, process and embed documents using various embedding services, and perform semantic searches across vector embeddings.
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    Enables semantic code search across codebases using Qdrant vector database and OpenAI embeddings, allowing users to find code by meaning rather than just keywords through natural language queries.
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    MIT License
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    Enables semantic search and document management using a local Qdrant vector database with OpenAI embeddings. Supports natural language queries, metadata filtering, and collection management for AI-powered document retrieval.
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    MIT License
    • Apple
    • Linux

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    This server enables semantic search capabilities using Qdrant vector database and OpenAI embeddings, allowing users to query collections, list available collections, and view collection information.
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    Provides semantic memory capabilities using Qdrant vector database with configurable embedding providers, allowing storage and retrieval of information using vector similarity.
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    Facilitates knowledge graph representation with semantic search using Qdrant, supporting OpenAI embeddings for semantic similarity and robust HTTPS integration with file-based graph persistence.
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    Enables storing and retrieving information using semantic search with Qdrant vector database. Acts as a memory layer for LLMs to persistently store and semantically search through information and metadata.
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    Apache 2.0
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    A Model Context Protocol server that provides semantic understanding of codebases using Qdrant vector database, enabling AI assistants to search files by purpose, discover relationships between files, analyze architecture, and identify refactoring opportunities.
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    A bridge server that connects Agent Communication Protocol (ACP) agents with Model Context Protocol (MCP) clients, enabling seamless integration between ACP-based AI agents and MCP-compatible tools like Claude Desktop.
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    MIT License
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    This Serper MCP Server supports search and webpage scraping, and all the most recent parameters introduced by the Serper API, like location.
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    MIT License
    • Apple
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    A Model Context Protocol server that enables LLMs to perform Google searches via the Serper API, allowing models to retrieve current information from the web.
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    Enables web search via Serper API with advanced search operators and webpage scraping capabilities to extract content in plain text or markdown format.
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    MIT License
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    Enables querying a hybrid system that combines Neo4j graph database and Qdrant vector database for powerful semantic and graph-based document retrieval through the Model Context Protocol.
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    MIT License
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    Provides intelligent memory management capabilities using Qdrant vector database for semantic search and storage. Supports global, learned, and agent-specific memory types with markdown processing and duplicate detection.
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    A demonstration MCP server that provides basic utility tools including hello world functionality and string reversal operations. Shows how to build and deploy MCP tools using the MCPO orchestrator framework.
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    GPL 3.0