MCP server for compressing AI embeddings by 5-7x using TurboQuant (PolarQuant + QJL), with tools to compress, decompress, estimate savings, and embed+compress vectors.
Enables semantic search across Apple Mail, Messages, Calendar, and Contacts on macOS using natural language queries. All processing happens locally with privacy-first vector indexing for fast similarity search.
An MCP server that integrates Crawl4AI with Supabase to enable AI agents to crawl websites, store content in a vector database, and perform RAG queries.
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 Claude to interact with core AWS services like S3, EC2, RDS, and CloudWatch, along with a generic SDK wrapper for any AWS operation. It also supports cost monitoring and optional vector store capabilities for document ingestion and search.
Enables AI agents to access enterprise data from Unity Catalog (vector search, functions, Genie spaces) and perform developer actions in Databricks like managing notebooks and running jobs.
Semantic memory for AI builders: capture the tacit engineering know-how that never reaches your docs, recall it the moment it applies. Built in Rust on Postgres and pgvector.
An MCP server for semantic search and retrieval of indexed Slack messages stored in Qdrant using Cohere reranking via AWS Bedrock. It enables users to search through Slack history, retrieve full message threads, and access channel or user statistics through natural language.
Integrates R2R (Retrieval-Augmented Generation) with Claude Desktop, enabling semantic search across knowledge bases and RAG-based question answering with support for vector, graph, web, and document search.
A server that enables vector and keyword search capabilities in Typesense databases through the Model Context Protocol, providing tools for collection management, document operations, and search functionality.
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 Claude to store and query personal finance transactions using semantic search. Transactions are persisted in ChromaDB and JSON, allowing natural language questions about spending trends and portfolio allocations.
An MCP server for querying and managing LlamaIndex documents stored in Qdrant vector databases, with automatic embedding model detection and extensive tools for search, retrieval, and collection management.
Enables fetching relevant content and embeddings from Supavec via the Model Context Protocol, allowing AI assistants like Claude to access vector search capabilities.
Provides advanced document search and processing capabilities through vector stores, including PDF processing, semantic search, web search integration, and file operations. Enables users to create searchable document collections and retrieve relevant information using natural language queries.
A local knowledge base system based on ChromaDB that supports automatic chunking, vector storage, and efficient similarity retrieval of txt and pdf documents, with MCP protocol support allowing AI assistants to directly access knowledge management functions.
Enables AI assistants to interact with MariaDB databases through standard SQL operations and advanced vector/embedding-based search. Supports database management, schema inspection, and semantic document storage and retrieval with multiple embedding providers.