Enables AI assistants to access the Open Food Facts database to query detailed food product information, nutritional data, and environmental scores. Supports product lookup by barcode, smart search with filtering, nutritional analysis, product comparison, and dietary recommendations to help users make informed food choices.
Provides comprehensive food hierarchy and nutrition data through structured tools that enable searching foods, browsing categories, and retrieving detailed nutritional information from a MongoDB Atlas database.
Enables tracking food intake and nutrition using the USDA FoodData Central database. Supports logging meals, setting daily nutrition goals, viewing food diaries, and analyzing nutrition trends over time with local SQLite storage.
MCP server for local semantic search over web content, enabling AI agents to ingest, index, and query pages with hybrid retrieval and token budget control.
A Model Context Protocol server that exposes a hybrid RAG pipeline (dense+sparse retrieval with reranking) for querying an enterprise knowledge base, enabling autonomous agents to search and retrieve relevant information.
Local-first MCP server enabling cross-modal search across text, images, documents, video, and audio transcripts. Provides 26 tools for ingesting, searching, and navigating local file systems with a 3-stage pipeline including reranking.
Local-first RAG MCP server that indexes local documents and provides hybrid search (semantic + keyword) with file ingestion, URL fetching, and a CLI, keeping data on-device.
An MCP server that enables hybrid semantic and keyword retrieval over your documents, using PostgreSQL and pgvector as the backend. It fuses rankings from both methods to provide high-quality search results to the language model.
