"Creating a Postgres MCP server in Python" matching MCP servers:

Zero-config PostgreSQL MCP server with auto-provisioning. Drop-in replacement for @modelcontextprotocol/server-postgres — no DATABASE_URL needed.

Enables users to ingest PDF/DOCX/TXT/MD documents and ask natural language questions about them, using local embeddings and Groq-powered retrieval-augmented generation.

Enables secure, AI-driven PostgreSQL database administration, observability, and querying with support for extensions like PostGIS and pgvector, connection pooling, and advanced tool filtering.

An MCP server that analyzes Python codebases using AST, stores code elements in a vector database, and enables natural language queries about code structure and functionality using RAG with Google's Gemini models.

A server that provides access to Baidu Cloud Vector Database functionality through the Model Context Protocol, enabling LLM applications to perform vector searches and database operations via natural language.

Agentic RAG Knowledge Assistant is a secure, tenant-isolated MCP server built with FastAPI, PostgreSQL, and pgvector that enables document ingestion, semantic retrieval, and vector search over PDF, DOCX, and text files through authenticated MCP tools.

Enables semantic search and management of Zotero reference libraries using PostgreSQL with pg-vector and OpenAI/Ollama embeddings. Provides AI-powered search, full-text extraction, metadata access, annotations, notes, tags, and collections management.

MCP RAG Server is a Python MCP server that indexes documents in multiple formats (Markdown, text, PowerPoint, PDF) using multilingual-e5-large embeddings and enables vector search for retrieval-augmented generation.

The MCP Server for Weaviate facilitates integration with Weaviate using a customizable Python-based server, enabling interaction with Weaviate databases and OpenAI APIs via configurable URL and API keys.

Provides MCP tools to upsert and search vector-embedded note chunks, enabling semantic retrieval from a PostgreSQL/pgvector backend.

Enables AI assistants to store and retrieve long-term memories using PostgreSQL with vector similarity search. Supports semantic memory operations, tagging, and real-time updates for persistent learning across conversations.

Python MCP server for vector search using Qdrant vector database and Ollama embeddings, with advanced query techniques like query expansion, HyDE, and reranking.

An MCP server that integrates with LangChain and ChromaDB to provide documentation search for AI libraries and vector database management.

Provides semantic search capabilities by connecting Claude Desktop to a Cloudflare Workers backend powered by Vectorize. It enables natural language querying of knowledge bases using vector similarity and edge-based embedding generation.

A server that enables vector and keyword search capabilities in Typesense databases through the Model Context Protocol, providing tools for collection management, document operations, and search functionality.

mcp-server-test

An MCP server that provides semantic memory on top of Qdrant vector search, with tools to store and retrieve information using embeddings.

A local-first knowledge base server that enables AI clients to store, retrieve, and manage documents using semantic search. Provides privacy-focused, offline-capable memory for AI assistants with tools for ingesting, querying, updating, and deleting knowledge.

Enables AI agents to autonomously create and manage topic-specific vector knowledge bases with end-to-end functionality including project creation, content ingestion from URLs, semantic search, and progress tracking. Provides a complete research workflow without exposing low-level APIs.

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.