ragmap

ragmap

MapRag is a discovery + routing layer for retrieval. It indexes RAG-capable MCP servers, enriches them with structured metadata, and helps agents (and humans) quickly find the right retrieval server for a task under constraints like citations, freshness, privacy, domain, and latency. MapRag does not do RAG itself. It helps you choose the best RAG tool/server to do the retrieval.

Category
Visit Server

README

RAGMap (RAG MCP Registry Finder)

Glama

RAGMap is a lightweight MCP Registry-compatible subregistry + MCP server focused on RAG-related MCP servers.

It:

  • Ingests the official MCP Registry, enriches records for RAG use-cases, and serves a subregistry API.
  • Exposes an MCP server (remote Streamable HTTP + local stdio) so agents can search/filter RAG MCP servers.

MapRag (RAGMap)

MapRag is a discovery + routing layer for retrieval. It helps agents and humans answer: which retrieval MCP server should I use for this task, given my constraints?

RAGMap does not do retrieval itself. It indexes and enriches retrieval-capable servers, then routes you to the right tool/server.

What you get after install (plain English)

  • You get discovery/routing tools (rag_find_servers, rag_get_server, rag_list_categories, rag_explain_score).
  • RAGMap helps you find the best retrieval server for your task and constraints.
  • Your agent then connects to that chosen server to do the actual retrieval.

RAGMap does not:

  • Ingest your private documents automatically.
  • Host your personal vector database.
  • Replace your end-to-end RAG pipeline.

If you need retrieval over your own data, use a retrieval server from RAGMap results (or your own server) that supports your ingest/index flow.

Freshness and ingest

  • Hosted RAGMap updates its index on a schedule. Newly published/changed servers may appear with some delay.
  • Most users do not run ingest themselves when using the hosted service.
  • If you need tighter freshness control or private indexing behavior, self-host and run your own ingest schedule (docs/DEPLOYMENT.md).

Features: Registry-compatible API; semantic + keyword search (when OPENAI_API_KEY is set, e.g. from env or your deployment’s secret manager); categories and ragScore; filter by hasRemote, reachable (HEAD-checked), citations, localOnly, transport, minScore, categories. Human browse UI at ragmap-api.web.app/browse — search, filter, copy Cursor/Claude config. MCP tools: rag_find_servers, rag_get_server, rag_list_categories, rag_explain_score.

Full overview: docs/OVERVIEW.md

Architecture

RAGMap architecture diagram

<details> <summary>Mermaid source</summary>

%%{init: {"theme":"base","themeVariables":{"primaryColor":"#ffffff","primaryTextColor":"#000000","primaryBorderColor":"#000000","lineColor":"#000000","secondaryColor":"#ffffff","tertiaryColor":"#ffffff","clusterBkg":"#ffffff","clusterBorder":"#000000","edgeLabelBackground":"#ffffff"},"flowchart":{"curve":"linear","nodeSpacing":75,"rankSpacing":70}}}%%
flowchart TB
  %% Concept-only diagram (product value; no deployment/framework/datastore details)

  classDef mono fill:#ffffff,stroke:#000000,color:#000000,stroke-width:1px;

  subgraph Inputs[" "]
    direction LR

    subgraph Query["Agent-native interface"]
      direction TB
      Users["Agents + humans"]:::mono
      subgraph Tooling["Tool call"]
        direction LR
        Criteria["Routing constraints<br/>domain, privacy, citations,<br/>freshness, auth, limits"]:::mono
        Tools["MCP tools<br/>rag_find_servers<br/>rag_get_server<br/>rag_list_categories<br/>rag_explain_score"]:::mono
      end
      Users --> Criteria --> Tools
    end

    subgraph Subregistry["Subregistry (read-only)"]
      direction TB
      subgraph Ingest["Ingest"]
        direction LR
        Sources["Upstream MCP registries<br/>(official + optional)"]:::mono
        Sync["Sync + normalize<br/>(stable schema)"]:::mono
        Catalog["Enriched catalog<br/>(servers + versions)"]:::mono
        Sources --> Sync --> Catalog
      end

      subgraph Enrich["Enrich (adds value)"]
        direction LR
        Cap["Structured metadata<br/>domain: docs|code|web|mixed<br/>retrieval: dense|sparse|hybrid (+rerank)<br/>freshness: static|continuous (max lag)<br/>grounding: citations|provenance<br/>privacy/auth: local|remote + req|optional<br/>limits: top_k|rate|max ctx"]:::mono
        Trust["Trust signals (lightweight)<br/>status, reachability,<br/>schema stability, reports"]:::mono
      end

      Catalog --> Cap
      Catalog --> Trust
    end
  end

  subgraph Selection["Selection (the added value)"]
    direction LR
    Router["Router<br/>match + rank + explain"]:::mono
    Ranked["Ranked candidates<br/>+ reasons + connect info"]:::mono
    Retrieval["Chosen retrieval MCP server(s)<br/>(do retrieval)"]:::mono
    Router --> Ranked --> Retrieval
  end

  Tools --> Router
  Catalog --> Router

  %% Keep the layout without adding a third visible "box" around Inputs.
  style Inputs fill:#ffffff,stroke:#ffffff,stroke-width:0px

</details>

Monorepo layout

  • apps/api: REST API + MCP registry-compatible endpoints + ingestion worker
  • apps/mcp-remote: Remote MCP server (Streamable HTTP)
  • packages/mcp-local: Local MCP server (stdio)
  • packages/shared: Zod schemas + shared types
  • docs: docs + Firebase Hosting static assets

Local dev

cp .env.example .env
corepack enable
pnpm -r install
pnpm -r dev

Optional: set OPENAI_API_KEY in .env (see .env.example) to enable semantic search locally; GET /health will show "embeddings": true.

API: http://localhost:3000 MCP remote: http://localhost:4000/mcp

Ingest

curl -X POST http://localhost:3000/internal/ingest/run \
  -H "Content-Type: application/json" \
  -H "X-Ingest-Token: $INGEST_TOKEN" \
  -d '{"mode":"full"}'

MCP usage

Remote (Streamable HTTP):

claude mcp add --transport http ragmap https://<your-mcp-domain>/mcp

Local (stdio, npm):

npx -y @khalidsaidi/ragmap-mcp@latest

Local (stdio):

pnpm -C packages/mcp-local dev

Key endpoints

  • GET /embed — embeddable “Search RAG MCP servers” widget (iframe; query params: q, limit)
  • GET /health (includes embeddings: true|false when semantic search is on/off)
  • GET /readyz
  • GET /v0.1/servers
  • GET /v0.1/servers/:serverName/versions
  • GET /v0.1/servers/:serverName/versions/:version (supports latest)
  • GET /rag/search
  • GET /rag/categories
  • GET /api/stats (public usage aggregates; no PII)
  • GET /api/usage-graph (HTML chart of usage)
  • POST /internal/ingest/run (protected)

For hosted ragmap-api.web.app, /internal/* routes are not exposed publicly.

GET /rag/search query params:

  • q (string)
  • categories (comma-separated)
  • minScore (0-100)
  • transport (stdio or streamable-http)
  • registryType (string)
  • hasRemote (true or false — only servers with a remote endpoint)
  • reachable (true — only servers whose streamable-http URL passed a HEAD check)
  • citations (true — only servers that mention citations/grounding in metadata)
  • localOnly (true — only stdio, no remote)

Smoke tests

API_BASE_URL=https://ragmap-api.web.app ./scripts/smoke-public.sh
MCP_URL=https://ragmap-api.web.app/mcp ./scripts/smoke-mcp.sh

Docs

  • docs/DISCOVERY-LINK-CONVENTION.md — optional discoveryService in server.json so clients can show “Discover more”
  • docs/AGENT-USAGE.mdfor agents: discovery, REST API, MCP install (no human intervention)
  • docs/DEPLOYMENT.md
  • docs/OVERVIEW.md
  • docs/DATA_MODEL.md
  • docs/PRIVACY.md
  • docs/PUBLISHING.md
  • docs/GLAMA-CHECKLIST.md
  • docs/GLAMA-DOCKERFILE.md
  • scripts/glama-score-status.sh — print public Glama score flags (inspectable/release/usage)

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured