Skip to main content
Glama
68,702 servers. Last updated

Matching MCP tools:

Matching MCP Connectors:

"Information about RAG (Retrieval-Augmented Generation)" matching MCP servers:

  • A
    license
    B
    quality
    D
    maintenance
    Enables retrieval-augmented generation by embedding queries with a chosen provider (e.g., OpenAI) and searching supported vector stores (Pinecone, pgvector) to return relevant content.
    Last updated
    1
    Apache 2.0
  • F
    license
    A
    quality
    C
    maintenance
    An MCP server that provides information about Utkarsh, including bio, skills, work experience, and portfolio projects, accessible via local stdio or remote HTTP with OAuth.
    Last updated
    6
  • F
    license
    A
    quality
    B
    maintenance
    An MCP (Model Context Protocol) server that gives AI agents live, structured ad intelligence across Facebook, Google, and Instagram — data that no base model can produce from training alone. Powered by Apify actors. Works with any MCP-compatible client: Cursor, Claude, etc.
    Last updated
    11
  • A
    license
    A
    quality
    D
    maintenance
    Provides 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 updated
    4
    34
    1
    MIT
  • F
    license
    C
    quality
    D
    maintenance
    A server that implements Retrieval-Augmented Generation using GroundX and OpenAI, enabling semantic search and document retrieval with Modern Context Processing for enhanced context handling.
    Last updated
    3
  • A
    license
    A
    quality
    A
    maintenance
    A multi-agent Retrieval-Augmented Generation system exposed as an MCP server. Ask a question and a LangGraph pipeline plans the retrieval, pulls evidence from a pgvector knowledge base, optionally augments it with live web research, drafts a cited answer, and then self-critiques it for grounding — revising until the answer is supported by the sources.
    Last updated
    3
    1
    MIT
  • F
    license
    A
    quality
    D
    maintenance
    Enables indexing local documents (PDF, Markdown, text, code) into a knowledge base and querying them via semantic search using local embeddings, all running privately on your machine.
    Last updated
    4
  • A
    license
    A
    quality
    B
    maintenance
    Enables ingestion and semantic search over text documents using PostgreSQL + pgvector and OpenAI-compatible embeddings, allowing any LLM agent to retrieve relevant chunks for grounded answers.
    Last updated
    4
    AGPL 3.0
  • A
    license
    A
    quality
    D
    maintenance
    A Model Context Protocol (MCP) server that provides a local-first RAG engine for your markdown documents. It uses a file-based Milvus vector database to index your notes, enabling LLMs to perform semantic search and retrieve relevant content from your local files.
    Last updated
    3
    56
    Apache 2.0
  • F
    license
    A
    quality
    B
    maintenance
    A local-first document retrieval engine that mounts as an MCP tool for agents to index files, search for relevant passages, and let the agent's own LLM answer.
    Last updated
    4
  • A
    license
    B
    quality
    C
    maintenance
    Enables storing and retrieving text passages based on semantic meaning using local embeddings (Ollama) and vector storage (ChromaDB), allowing conversational memorization and retrieval of information.
    Last updated
    5
    18
    MIT
  • A
    license
    A
    quality
    C
    maintenance
    MCP server that integrates with LM Studio to provide a search_notes tool, allowing the chat model to retrieve and answer from a local Obsidian vault.
    Last updated
    1
    MIT