358 published MCP servers in this category · page 5 of 6 · all categories
Patent search, USPTO data, patent landscape & pgvector prior-art search for agents.
Extract text, tables and RAG chunks from public PDF URLs; pay-per-event via your Apify account.
x402 PDF text, pages, and metadata for AI/RAG. $0.0015 per successful PDF.
Convert PDF, Word, Excel and scanned documents to Markdown, tables and RAG chunks. OCR.
Pergamos is a read-only MCP server that lets Claude Desktop search and inspect a Calibre library through the Calibre Content Server.
Local-first MCP memory server: SQLite, FTS5 and vector search, AES-256-GCM. 49 tools, no cloud.
Similarity search, hybrid search, and index management for pgvector-backed PostgreSQL tables
Create and manage vector database indexes, backups, and collections
An MCP server implementation for retrieving information from Pinecone Assistant.
Model Context Protocol server for Pinecone - enables AI assistants to interact with Pinecone indexes and documentation
Docs + source RAG for Pipe language: semantic search, cited answers, code symbol lookup.
AI-native runtime with built-in MCP server. 232 builtins, AI pipelines, RAG, single ~8 MB binary.
Shared cross-LLM long-term memory over MCP: semantic recall, sessions, and media (pgvector).
MCP server for Prompt Builder — search, retrieve, and compile prompt components from a community vault using semantic search (pgvector) and
Scans RAG content/scraped pages for indirect prompt injection
Multi-LLM orchestration framework with agentic execution, memory/RAG, and training data generation
PyChroma MCP connects a local ChromaDB collection to an assistant for document ingestion, embedding configuration and semantic retrieval.
PyMuPDF Utilities for LLM/RAG
支持查询主流agent框架技术文档的MCP server（支持stdio和sse两种传输协议）, 支持 langchain、llama-index、autogen、agno、openai-agents-sdk、mcp-doc、camel-ai 和 crew-ai
Implement semantic memory layer on top of the Qdrant vector search engine
📝 **[Changelog v1.0.4](./CHANGELOG.md)** - Latest improvements and bug fixes
MCP server that wraps the Qdrant vector database API as tools.
ローカル環境でQdrantとMCP-server-qdrantを簡単に立ち上げるためのDocker Compose設定です。
A Model Context Protocol (MCP) server that provides semantic code search capabilities using Qdrant vector database and OpenAI embeddings.
An MCP server for interacting with Qdrant vector database. This server provides tools for managing vectors, performing similarity searches, and automatic text-to-vector embedding using the MCP…
A powerful knowledge management system that forges wisdom from experiences, insights, and best practices. Built with Qdrant vector database for efficient knowledge storage and retrieval.
Persistent scoped Qdrant memory for agents, through four explicit stdio MCP tools.
A context-aware Model Context Protocol (MCP) server that provides semantic search capabilities across your codebase using Qdrant vector database. Now
Role-based access to your Qdrant vector store, via MCP. OIDC in, scoped JWTs out.
MCP server for semantic search with Qdrant vector database
MCP RAG server with hybrid search, multi-KB support, and AI-powered chunk contextualization.
Local RAG MCP server with hybrid search, PDF/DOCX support, and zero-config setup
Repository intelligence, code graph, history, papertrail, and cross-agent memory for coding agents.
MCP server for rag knowledge. Features semantic search, knowledge graph query, index documen...
Rag Knowledge Graph automation via MCP. Includes index document, rag query, add graph edge. ...
Dynamic RAG Engine preventing AI hallucinations in Korean Finance and Crypto markets.
Building an Rag-based HR chatbot for providing rules in workplace with MCP server
Build a RAG pipeline station by station and search your own documents with cited answers.
Diagnose RAG drift: interpret scores, recommend thresholds, explain dimensions.
Simple RAGFlow MCP. Only useful until the RAGFlow team releases the official MCP server
A simple MCP server of knowledge base for RAGFlow.
Ingest, manage, and retrieve documents for RAG-powered AI applications
A Model Context Protocol (MCP) server for Ragie
RAGMap is a lightweight MCP Registry-compatible subregistry + MCP server focused on **RAG-related MCP servers**.
Minimal RAG-over-a-corpus MCP retrieval: search_knowledge returns cited chunks. Local embeddings.
RAG retrieval IR metrics: recall@k, hit@k, MRR, NDCG@k, evaluate_batch.
Search your knowledge bases from any AI assistant using hybrid RAG.
Generate QA datasets & evaluate RAG systems. Privacy-first, any LLM, local or cloud.
RAG-as-a-service MCP sunucusu — çok-kiracılı koleksiyon yönetimi, metin ingest (chunk+embed+upsert,…
Search and chat with a RAGStack knowledge base through its MCP server, with tools for document uploads and website ingestion.
Index your docs, files, and websites into a vector store and give your AI agent live semantic search with auto-syncs on changes, zero code required.
Implementation of an MCP server for the RAG Web Browser Actor. This Actor serves as a web browser for large language models (LLMs) and RAG pipelines, similar to a web search in ChatGPT.
Agent memory on your own Postgres that flags superseded facts and abstains instead of guessing.
Web design analysis with 26 MCP tools: layout, motion, quality, semantic search via pgvector
Persistent knowledge memory for AI agents. Hybrid search, code graph, pgvector.
GitHub repository vectorizer and context synthesizer for RAG pipelines.
AI agents using Markdown + RAG score 0% recall on "why?" questions. The same agents using Right Reasons score 100%. Tested across 48 sessions with 2
Not just vector search — robotmem understands the structure of robot experiences:
HTTP MCP server for S3 documentation with RAG using HNSWLib and Ollama
Salesforce AgentForce knowledge graph — 40 nodes, Einstein Trust Layer, 11x fewer tokens than RAG.