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

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

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

Acts as a secure bridge connecting PostgreSQL databases to AI models, enabling natural language querying, schema analysis, and controlled write operations with multi-layer security.

Enables SQL queries against PostgreSQL databases through MCP-compatible clients and includes a natural language agent for forming SQL queries from natural language.

An open-source MCP server that provides AI agents with advanced PostgreSQL capabilities including index tuning, query plan optimization, and comprehensive database health analysis. It supports safe SQL execution through configurable access modes and offers both stdio and SSE transport options for various development environments.

Enables natural language queries to a Neo4j knowledge graph built from PostgreSQL data, integrating with Claude Desktop and REST API.

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.

Enables AI agents to execute SQL queries and introspect PostgreSQL schemas, tables, and indexes with read-only safety by default. Supports optional write operations and works with Claude, LangChain, and other agents via stdio or HTTP transports.

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

All In One Model Context Protocol: qdrant, google suite (gmail, calendar, ...), jira, gitlab, CLI, ...

Enables AI agents to execute any SQL command (including DDL/DML) on a PostgreSQL database with zero restrictions, allowing full database control and dynamic database switching.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

A server that connects Claude to your documentation via Inkeep's API, enabling AI-powered interactions with your documentation content.

A Python-based MCP server that enables document-based question answering by processing PDF, TXT, and Markdown files through OpenAI's API. It provides hallucination-free responses based strictly on document content using semantic search and includes a web interface for management.

Production-ready MCP server providing RAG, hierarchical memory, and 8+ tools for AI agents via the Model Context Protocol.

A Node In Layers package that simplifies creation of MCP (Model-Control-Protocol) servers with tools for defining models, adding CRUD operations, and interacting with clients.

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.

A Model Context Protocol server providing dual transport (HTTP and Stdio) access to PostgreSQL databases, allowing AI assistants to query databases and fetch schema information through natural language.

A PostgreSQL MCP server that automatically detects and obfuscates personally identifiable information (PII) in query results using column-name heuristics and NLP analysis. It enables AI agents to interact securely with databases by masking sensitive data by default while allowing selective unmasking under user control.

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.