"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.

Provides Claude Code read-only access to PostgreSQL for relational queries, schema inspection, EXPLAIN plans, pgvector similarity searches, and Apache AGE graph traversals.

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.

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.

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.

It connects an agent to a PostgreSQL database so it can inspect schemas, run validated reads and writes, resolve fuzzy names via trigram search, and perform or store pgvector embedding searches. Vector and hybrid search operate through an OpenAI-compatible embeddings endpoint, letting agents query by text without handling embedding arrays.

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.

A Python MCP server that enables semantic search through Search Labs blog posts indexed in Elasticsearch, allowing Claude to intelligently retrieve relevant information from the blog content.

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.

Enables persistent memory storage and retrieval for AI conversations using Mem0, with semantic search capabilities backed by local Postgres and Qdrant vector database.

将 pgvector 知识库检索能力封装为标准 MCP 服务器，支持 Tools、Resources 和 Prompts，使 Claude Desktop 等客户端可通过自然语言查询知识库。

Enables LLMs to interact with Zvec vector database through tools for collection management, document operations, vector search, and AI-powered embeddings.

Lets AI assistants connect to Apache Solr deployments to enumerate collections and schemas, run full-text and filtered queries, apply faceting and sorting, and issue SQL or vector-semantic searches without building custom API glue. Communicates over stdio or SSE and requires minimal environment configuration instead of app-specific credentials or mandatory ZooKeeper wiring.

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.