mcp-server-qdrantofficial
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qualityThis repository is an example of how to create a MCP server for Qdrant, a vector search engine.
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615
Python
Apache 2.0
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Why this server?
This server implements a Model Context Protocol (MCP) server for Qdrant, a vector search engine, which aligns with the 'vector code' query.
Why this server?
This server provides semantic search over local git repositories, enabling users to search code through vectorized code chunks.
Why this server?
This server is a local vector database system that provides coding agents with fast, efficient semantic search capabilities for software projects.
Why this server?
This server allows AI tools like Claude Desktop to query databases via ODBC connections, allowing access, analysis, and insight generation from database data while maintaining security through read-only safeguards.
Why this server?
A Model Context Protocol (MCP) server designed to easily dump your codebase context into Large Language Models (LLMs).
Why this server?
A Model Context Protocol server that helps large language models index, search, and analyze code repositories with minimal setup
Why this server?
A Model Context Protocol server that enables LLMs to read, search, and analyze code files with advanced caching and real-time file watching capabilities.
Why this server?
Connects agents to Elasticsearch data using the Model Context Protocol, allowing natural language interaction with Elasticsearch indices through MCP Clients like Claude Desktop and Cursor.
Why this server?
SourceSage is an MCP (Model Context Protocol) server that efficiently memorizes key aspects of a codebase—logic, style, and standards—while allowing dynamic updates and fast retrieval. It's designed to be language-agnostic, leveraging the LLM's understanding of code across multiple languages.
Why this server?
A server that provides structured access to markdown documentation from NPM packages, Go Modules, or PyPi packages, enabling informed code generation by exposing these docs as resources or tools.