"A Python server using FastAPI to expose public web API endpoints to an agent" matching MCP servers:

MCP server that ingests PDF documents into pgvector for semantic search and RAG pipelines. It handles extraction, chunking, local embeddings, and storage, enabling agents to make PDFs searchable via natural language.

MCP server for semantic search over PDF documents using RAG, pgvector, and OpenAI embeddings. Includes a web app for document upload and management.

A lightweight RAG system that provides an MCP server for searching and interacting with vector-based knowledge bases. It enables users to perform retrieval-augmented generation and search across Qdrant collections through a standardized interface.

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 local-first knowledge base for LLM coding agents that indexes repository documentation, concept ontology, and build targets into Qdrant and exposes retrieval as MCP tools (search, get, list sources, reindex).

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.

A self-evolving RAG system that enables AI agents to autonomously read and write memory, continuously learning and adapting user preferences, daily logs, and knowledge graphs across applications.

Enables natural language search and analysis of uploaded PDF, CSV, and Excel documents using retrieval-augmented generation and MCP tools, providing contextual answers to user queries.

A benchmarking MCP server that stores facts as both vector RAG and a knowledge graph, enabling head-to-head comparison of memory retrieval methods.

Enables semantic search over OpenAPI endpoint catalogs using FAISS and LangGraph, returning grounded technical recommendations for natural-language API queries.

Enables hybrid search over policies using Reciprocal Rank Fusion and provides grounded, context-aware answers via a LangGraph agent with COSTAR prompting.

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.

Enables external clients to query a vector knowledge base about skills and roles through an MCP retrieval tool, supporting RAG-based question answering with tool-calling.

MCP server enabling Claude Desktop to answer questions from local Word and PDF documents by searching a vector index built from their contents.

Enables AI clients to search and analyze code/docs through a central Pinecone-backed vector store, inspect project inventories and git sync status, rank work by urgency, and perform deep architectural audits via MCP.

Enables AI agents to maintain long-term, cross-session memory by extracting facts, reconciling state conflicts, and retrieving relevant memories via vector search.

A local-first AI backend that combines Model Context Protocol, Retrieval-Augmented Generation, and an agent runtime to answer questions from a private knowledge base.

Official Kimss MCP server — call your Kimss agents, models, files, and vector stores from Cursor and other MCP clients using your API key.

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.