fri3d-badge-mcp

A Model Context Protocol server that gives LLM clients structured access to MicroPython documentation and the Fri3d Camp 2026 badge documentation.

It can run locally as a stdio MCP server (via npx) or be deployed to Vercel as an HTTP MCP endpoint using the mcp-handler package.

Sources

• MicroPython official docs — https://docs.micropython.org/en/latest/ (Sphinx site; we use its searchindex.js for keyword search and scrape individual pages for content.)
• MicroPythonOS docs — https://docs.micropythonos.com/ (MkDocs Material site; pages and sections discovered via search/search_index.json.)
• Fri3d Camp 2026 badge — https://fri3dcamp.github.io/badge_2026/ (MkDocs Material site; pages and sections discovered via search/search_index.json.)

Nothing is bundled or pre-indexed — everything is fetched on demand and cached in memory and on disk so the server stays current with upstream docs without redeploys.

Tools exposed

| Tool | Description | | ---- | ----------- | | search_micropython_docs | Keyword search across the MicroPython docs (Sphinx index). | | get_micropython_page | Fetch & clean a MicroPython doc page (path or full URL). | | list_micropython_modules | Curated list of common MicroPython modules + quick-references. | | search_fri3d_badge_docs | Substring search across all Fri3d badge_2026 pages. | | get_fri3d_badge_page | Fetch & clean a Fri3d badge_2026 page. | | list_fri3d_badge_pages | List all known Fri3d badge_2026 pages and sections. | | search_micropythonos_docs | Substring search across all MicroPythonOS docs pages. | | get_micropythonos_page | Fetch & clean a MicroPythonOS docs page. | | list_micropythonos_pages | List all known MicroPythonOS docs pages and sections. |

All tools return text content suitable for direct consumption by an LLM.

Local usage via npx (stdio transport)

The easiest way to use this MCP server locally is via npx. Add it to your MCP client configuration (e.g. Claude Desktop claude_desktop_config.json):

{
  "mcpServers": {
    "fri3d-badge": {
      "command": "npx",
      "args": ["fri3d-badge-mcp"]
    }
  }
}

Or start it manually to test:

npx fri3d-badge-mcp

Caching

The local server caches all fetched search indices and pages using a two-layer strategy:

| Layer | Scope | TTL | |-------|-------|-----| | In-memory | current process | 15 min – 1 h | | Disk (os.tmpdir()/fri3d-badge-mcp/) | survives restarts | same TTL |

Stale-while-revalidate: when the disk cache has data older than its TTL (but less than 2× TTL), the server returns the stale data immediately while refreshing in the background, so the next call already gets fresh results. Data older than 2× TTL is always re-fetched synchronously.

Architecture

api/
  server.ts            # Vercel MCP handler — wires tools to source modules
src/
  server.ts            # Standalone stdio MCP server (npx entry point)
  tools.ts             # Shared tool registrations (used by both transports)
  lib/
    cache.ts           # TTL cache with disk persistence + stale-while-revalidate
    fetch.ts           # fetch() wrapper with timeout + UA
    html.ts            # Lightweight HTML → markdown-ish text extractor
  sources/
    micropython.ts     # MicroPython search + page fetch
    fri3d.ts           # Fri3d badge docs search + page fetch
    micropythonos.ts   # MicroPythonOS docs search + page fetch
scripts/
  test-client.mjs      # Sample MCP client for local smoke testing
vercel.json            # Routes everything to /api/server, 60s max duration

Deploy on Vercel

1. Push this repo to GitHub.
2. Import it on Vercel and deploy. No env vars required.
3. Enable Fluid compute for better warm-start reuse of the in-memory cache.
4. (Pro/Enterprise) bump vercel.json maxDuration to 800 if you want long searches to never time out.

The MCP endpoint is then:

https://<your-deployment>.vercel.app/mcp

Uses streamable-HTTP transport only (the current MCP spec).

Local development (Vercel dev server)

npm install
npm start            # runs `vercel dev` on http://localhost:3000
node scripts/test-client.mjs http://localhost:3000

Notes & limitations

• The MicroPython search uses Sphinx's searchindex.js, which contains stemmed tokens. The implementation does prefix matching plus title/object weighting; it is intentionally simple but covers typical lookups (machine.Pin, interrupt, neopixel, wifi, …).
• The Fri3d badge_2026 and MicroPythonOS sources both use MkDocs Material's search/search_index.json and currently perform weighted substring matching over title and body text.
• Page fetches are restricted to the documentation hostnames as a small SSRF guard.