"Co-op" matching MCP servers:

Enhances large language models with competitive programming knowledge by leveraging OI-Wiki content through vector search, allowing models to retrieve relevant algorithms and techniques.

Vectorize MCP server for advanced retrieval, Private Deep Research, Anything-to-Markdown file extraction and text chunking.

Pinecone Assistant MCP server

Wraps n8n with an MCP server and vector storage to enable semantic search, management, and execution of automated workflows. It integrates with other tools to make workflows searchable and orchestratable within a larger automation ecosystem.

Provides search capabilities for Cloudflare AutoRAG instances, enabling AI assistants like Claude to directly search and query knowledge bases using three distinct search methods: basic search, rewrite search, and AI search.

🧠 High-performance persistent memory system for Model Context Protocol (MCP) powered by libSQL. Features vector search, semantic knowledge storage, and efficient relationship management - perfect for AI agents and knowledge graph applications.

A persistent semantic memory layer for AI coding agents, enabling storage and retrieval of text with vector embeddings via MCP tools.

Enables querying MaxKB knowledge bases through MCP, supporting vector, hybrid, and full-text retrieval of relevant segments, as well as listing knowledge bases, documents, and workspaces.

A Retrieval Augmented Generation MCP server that ingests documents into a local vector database and enables semantic search queries.

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.

Enables AI agents to search and retrieve Standard Operating Procedures using semantic search with Italian language support, powered by ChromaDB and RAG for intelligent document retrieval from categorized SOP collections.

Provides tools for ingesting documents into a local vector database and retrieving relevant information via semantic search, enabling retrieval-augmented generation for MCP clients.

Enables AI assistants to crawl websites, extract and store web content with semantic search capabilities using vector embeddings, and retrieve information through natural language queries with tag-based filtering and intelligent content cleaning.

Enables bidirectional synchronization of vector database collections between local and remote Qdrant instances with tools for comparison, dry-run previews, and sync operations in both directions.

A MCP server for document ingestion and semantic search using ChromaDB and sentence transformers, enabling users to query natural language over ingested documents.

Enables semantic search and analysis of customer support tickets. Provides tools to search tickets, analyze the dataset, and retrieve individual tickets using natural language.

Exposes a Neo4j knowledge-graph index over personal markdown notes to LLM agents via MCP, enabling semantic search, tag navigation, and relationship discovery.

Enables natural-language semantic search of a project's code through a local vector index, so questions that plain grep cannot match return ranked file:line references. It also indexes or refreshes projects on demand, reports index status, and optionally flags near-identical code across files.

Myelin provides persistent, brain-inspired memory for AI agents, enabling cross-session and cross-tool context retention with local, private storage.

Connect your Pinecone projects to Cursor, Claude, and other AI assistants