Hermes Atlas MCP Server
# πΊοΈ Hermes Atlas MCP Server
**MCP server for the [Hermes Atlas](https://hermesatlas.com) ecosystem directory** β gives AI agents instant access to 169+ quality-filtered tools, skills, plugins, and integrations for [Hermes Agent](https://github.com/NousResearch/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:
```json
{
"mcpServers": {
"hermes-atlas": {
"command": "npx",
"args": ["hermes-atlas-mcp"]
}
}
}
```
Or with Docker/stdio:
```json
{
"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:
```bash
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](https://github.com/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
```bash
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](https://github.com/ksimback/hermes-ecosystem) (MIT/CC BY 4.0).
TDQS
Scored across 8 tools
Each tool targets a distinct function: stats, featured repos, curated lists, single repo details, categories, personalized recommendations, and search. No two tools have overlapping purposes, minimizing agent confusion.
Most tools follow a verb_noun pattern with underscores (get_featured, get_list, search_repos), but ecosystem_stats uses noun_verb and recommend lacks an underscore, creating mild inconsistency.
Eight tools are appropriate for an ecosystem explorer, covering search, browsing, recommendations, and stats without being overwhelming or sparse.
The set covers key exploration tasks: searching, filtering by category or list, getting details, and recommendations. Missing is a bulk listing of all repos, but search_repos can substitute, so only minor gaps.