An MCP server that retrieves resume/experience evidence relevant to a job description via vector RAG, and tracks fit-analysis results in a configurable tracking store (Notion or SQLite), with tools like match_job, push_to_tracker, and list_applications.
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.
A flexible memory system for AI applications that supports multiple LLM providers and can be used either as an MCP server or as a direct library integration, enabling autonomous memory management without explicit commands.
An offline-first, governed memory and knowledge server for AI agents that provides Remember, Search, Update, and Forget operations with hybrid retrieval, semantic embeddings, and NID-based authentication. It can be used as an MCP server via stdio or Streamable HTTP, enabling agents to persist and query memories and wiki knowledge.
A high-performance MCP server for semantic search and codebase indexing using the Qdrant vector database. It features optimized embedding pipelines, AST-aware chunking, and git metadata enrichment for fast, privacy-focused local or remote search.
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 local vector database system that provides LLM coding agents with fast, efficient semantic search capabilities for software projects via the Message Control Protocol.
A focused local-paper RAG MCP server that imports PDFs, indexes them into SQLite and ChromaDB, and retrieves section-aware paper chunks for use with Claude Code, Codex, or any MCP client.
Provides AI agents with persistent, searchable memory using semantic search, auto-linking, and categorization, with zero-config local setup or production-ready external providers.
Upload documents (Word, Excel, PDF, PowerPoint) to a vector RAG store and perform semantic search with page-level citations. Queries are free; ingestion costs credits at break-even pricing.
RAG document search MCP server that allows AI assistants to search a local document set and retrieve grounded passages via keyword (SQLite FTS5) or semantic (Chroma) backends.
Enables semantic search and interaction with Fuel Network documentation and Sway Language resources within AI-powered development environments. It provides high-quality technical context using a local vector database to assist with smart contract development and FuelVM queries.
A Model Context Protocol (MCP) server that enables semantic search and retrieval of documentation using a vector database (Qdrant). This server allows you to add documentation from URLs or local files and then search through them using natural language queries.
A Model Context Protocol (MCP) server that enables LLMs to interact directly the documents that they have on-disk through agentic RAG and hybrid search in LanceDB. Ask LLMs questions about the dataset as a whole or about specific documents.