Hermes Atlas MCP Server
Enables AI agents to search, browse, and recommend tools from the Hermes Agent ecosystem directory with zero configuration.
README
πΊοΈ 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
ββββββββββββββββββββββββββββββββββββββββββββββ
β hermes-atlas-mcp β
β β
β ββββββββββββ ββββββββββββββββ β
β β repos β β summaries β β
β β (169) β β (AI-generatedβ β bundled β
β ββββββ¬ββββββ β per-repo) β or fetchedβ
β β ββββββββββββββββ β
β ββββββ΄βββββββββββββββββββββββ β
β β lists, featured, stats β β cached β
β βββββββββββββββββββββββββββββ (4hr TTL) β
β β
β βββββββββββββββββββββββββββββ optional β
β β chunks.json (70MB) β β install β
β β RAG knowledge base β separatelyβ
β βββββββββββββββββββββββββββββ β
ββββββββββββββββββββββββββββββββββββββββββββββ
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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