Hermes Atlas MCP Server

Hermes Atlas MCP Server

Enables AI agents to search, browse, and recommend tools from the Hermes Agent ecosystem directory with zero configuration.

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README

πŸ—ΊοΈ Hermes Atlas MCP Server

MCP server for the Hermes Atlas ecosystem directory β€” gives AI agents instant access to 169+ quality-filtered tools, skills, plugins, and integrations for Hermes Agent.

Why?

Hermes Agent has a massive and growing ecosystem. This MCP server turns that ecosystem into instant expandability β€” agents can discover, compare, and recommend tools without leaving their conversation.

Quick Start

Add to your MCP client config:

{
  "mcpServers": {
    "hermes-atlas": {
      "command": "npx",
      "args": ["hermes-atlas-mcp"]
    }
  }
}

Or with Docker/stdio:

{
  "mcpServers": {
    "hermes-atlas": {
      "command": "node",
      "args": ["/path/to/hermes-atlas-mcp/dist/index.js"]
    }
  }
}

Tools

Tool Description Example
search_repos Full-text search across 169 repos search_repos("memory persistence")
list_categories Browse 12 ecosystem categories list_categories()
get_repo Detailed repo info + AI summary get_repo("NousResearch/hermes-agent")
recommend Match tools to your use case recommend("I need to deploy on K8s")
get_featured Trending/rising repos this week get_featured()
get_lists Curated lists overview get_lists()
get_list Specific curated list with per-repo descriptions get_list("best-memory-providers")
ecosystem_stats Aggregate stats, category breakdown, latest version ecosystem_stats()
ask_atlas RAG over research knowledge base (requires embeddings) ask_atlas("How do skills work?")

Optional: Local Embeddings

The ask_atlas tool provides RAG-powered answers grounded in 27 research files (6,500+ chunks) covering Hermes Agent installation, architecture, skills system, deployment, and best practices.

Install the embeddings (~70MB) separately:

npx hermes-atlas-mcp install-embeddings
# or equivalently:
npx hermes-atlas-install

The server auto-detects the embeddings at startup and adds the ask_atlas tool when available. Without embeddings, all other tools work perfectly using the summaries index.

How It Works

β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚           hermes-atlas-mcp                β”‚
β”‚                                            β”‚
β”‚  β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”  β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”           β”‚
β”‚  β”‚ repos    β”‚  β”‚ summaries    β”‚           β”‚
β”‚  β”‚ (169)    β”‚  β”‚ (AI-generatedβ”‚  ← bundled β”‚
β”‚  β””β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”˜  β”‚  per-repo)  β”‚  or fetchedβ”‚
β”‚       β”‚        β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜           β”‚
β”‚  β”Œβ”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”             β”‚
β”‚  β”‚ lists, featured, stats   β”‚ ← cached    β”‚
β”‚  β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜   (4hr TTL) β”‚
β”‚                                            β”‚
β”‚  β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”  optional   β”‚
β”‚  β”‚ chunks.json (70MB)        β”‚ ← install   β”‚
β”‚  β”‚ RAG knowledge base        β”‚   separatelyβ”‚
β”‚  β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜             β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
          β”‚
    stdio (MCP)
  • Zero-config: Works immediately with no API keys needed
  • Offline-capable: Bundled data works without network; fresh data fetched in background
  • Light: Core data is ~300KB; embeddings are opt-in at 70MB
  • Fast: Full-text search and recommendations complete in <50ms

Data Sources

All data sourced from ksimback/hermes-ecosystem β€” a community-curated directory security-reviewed before inclusion.

File Size Content
repos.json 60KB 169 repos β€” owner, name, description, stars, category, official flag
summaries.json 189KB AI-generated summaries + highlights per repo
lists.json 2KB 6 curated lists (best memory, top skills, deployment, etc.)
list-summaries.json 26KB Per-repo descriptions within each curated list
featured.json 241B Currently featured/trending repos
latest-release.json 374B Latest Hermes Agent version
chunks.json 70MB 6,554 research chunks with pre-computed embeddings (optional)

Development

git clone https://github.com/your-user/hermes-atlas-mcp.git
cd hermes-atlas-mcp
npm install
npm run build

# Test interactively
echo '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"test","version":"0.1"}}}' | npm start

# Watch mode
npm run dev

License

MIT. Data sourced from hermes-ecosystem (MIT/CC BY 4.0).

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