Ask questions about memory files using retrieval-augmented generation to get answers from stored content with configurable search modes.
MIT
136,001 tools. Last updated 2026-05-17 13:44
"Understanding RAG (Retrieval-Augmented Generation or related topics)" matching MCP tools:
- Create a named local vector index for retrieval-augmented generation. Documents added are embedded via Ollama for local RAG without cloud dependencies.
- Stores a knowledge fragment with source and evidence tier metadata for future retrieval via semantic RAG queries.MIT
- Parse code files into semantic chunks such as functions, classes, and methods to improve retrieval in RAG systems.MIT
- Upload files to process and index them for searchable knowledge retrieval using RAG (Retrieval-Augmented Generation) technology.MIT
- Execute RAG queries against Amazon Bedrock Knowledge Bases to retrieve relevant documents using vector search for enhanced information retrieval.MIT
Matching MCP Servers
- Alicense-qualityCmaintenanceEnhances AI model capabilities with structured, retrieval-augmented thinking processes that enable dynamic thought chains, parallel exploration paths, and recursive refinement cycles for improved reasoning.Last updated23MIT
- AlicenseAqualityCmaintenanceProvides local Retrieval-Augmented Generation (RAG) capabilities using Ollama for embeddings and ChromaDB for vector storage. It enables users to ingest and perform semantic searches across PDF, Markdown, and TXT documents within MCP-compatible clients.Last updated4361MIT
Matching MCP Connectors
Medical RAG: semantic search for clinical guidelines, drug interactions, diagnoses & EHR data.
Medical RAG: semantic search for clinical guidelines, drug interactions, diagnoses & EHR data.
- Search uploaded documents using RAG to find answers with citations. Ask questions to retrieve information from your knowledge base.MIT
- Perform semantic search and question-answering on your Obsidian vault using a local retrieval-augmented generation pipeline.MIT
- Delete files from the RAG system to manage storage and maintain relevant content for retrieval-augmented generation tasks.MIT
- Add files to a RAG system for document retrieval, supporting PDF, DOCX, TXT, MD, CSV, and JSON formats to enable semantic search and information access.MIT
- Search and filter RAG-capable MCP servers from the RAGMap registry to find the right retrieval tool based on categories, transport type, citations, and other constraints.
- Retrieve statistics about the Retrieval-Augmented Generation system's performance and usage metrics to monitor and analyze its operational data.MIT
- Execute complete RAG workflows to answer questions using document context. Handles embedding generation, semantic search, and context retrieval automatically for Teradata databases.MIT
- Answer questions about Commodore 64 documentation by retrieving and synthesizing information from multiple sources. Provides answers with citations and confidence scores.
- Query documents with context using a Retrieval-Augmented Generation (RAG) system. Automatically creates an index if it does not exist, enabling quick access to relevant information from stored repositories and text files.
- Retrieve connected documentation topics including previous, next, parent, and related links for Synergy/DE development reference.
- Lists available documentation topics to help you identify relevant guides before searching or reading specific topics.MIT
- Execute a full RAG workflow to answer user questions by embedding queries, searching semantically, and returning context-grounded answers from documents.MIT
- Fetch Google Trends data including interest over time, geographic breakdowns, and related topics/queries. Supports multiple queries, custom dates, and categories for demand forecasting and seasonality analysis.MIT
- Filter and retrieve Manticore Search documentation files using regex patterns to efficiently find multiple topics in a single call.Apache 2.0