docs-to-mcp

Turn a documentation site into an MCP server your coding agent can search.

Crawl the docs once with Firecrawl, normalize the pages into a local corpus, and serve them over FastMCP — so the agent queries the real docs instead of recalling a version of them from training data.

Pipeline

firecrawl map + scrape  ->  ingest (normalize + frontmatter)  ->  sqlite FTS5 index  ->  MCP server
      crawler.py                    ingester.py                      index.py            server.py

All three build steps run behind one pipeline.refresh() entry point, reused by the CLI (docs-to-mcp crawl) and the MCP refresh_docs tool.

Related MCP server: docs-mcp

Requirements

Python 3.11+, uv, and a Firecrawl API — either one works:

• Hosted — an API key from firecrawl.dev (has a free tier). Set FIRECRAWL_API_URL=https://api.firecrawl.dev and FIRECRAWL_API_KEY=<key>.

• Self-hosted — follow Firecrawl's self-host guide. It defaults to http://localhost:3002, which is what this tool assumes when FIRECRAWL_API_URL is unset. Make sure the search backend (searxng) is up: without it Firecrawl can scrape a URL you hand it but cannot discover pages.

Usage

uv sync

# Crawl a docs site into data/docs/<slug>/
uv run docs-to-mcp crawl https://opencode.ai/docs --slug opencode-docs --max-pages 20

# Serve the captured corpus over MCP (stdio)
uv run docs-to-mcp serve --slug opencode-docs

# Rebuild the search index from pages already captured — no network, no re-crawl
uv run docs-to-mcp reindex --slug opencode-docs

When search says the index is stale

If a query fails with "the index was built by an older version", your captured pages are fine — only the index predates a schema change. Run reindex (seconds, offline). Do not re-crawl: that would re-fetch every page and spend real Firecrawl budget to repair something purely local.

Category grouping (MediaWiki)

For MediaWiki sites, tag captured articles with their categories so list_docs can filter by group (Weapons, Characters, …) and category names feed search ranking. Run after a crawl; it scrapes the Category namespace and rebuilds the index:

uv run docs-to-mcp categories --slug vampire-survivors --concurrency 6

Category membership is read from the Category namespace because main-content extraction strips the per-article category footer.

Discovery: sitemap first, link map as fallback

Discovery is resolved automatically and deterministically, so you don't have to figure out a site's shape yourself:

1. an explicit --sitemap <url> if you pass one;

2. else a sitemap auto-resolved from the site's robots.txt (Sitemap: directive);

3. else Firecrawl's link map.

MediaWiki is handled automatically. MediaWiki publishes a sitemap index split by namespace (NS_0 = content, NS_1 = Talk, NS_10 = Template, NS_828 = Module); the tool keeps only NS_0. So a wiki just works with no extra flags:

uv run docs-to-mcp crawl https://vampire.survivors.wiki/ --slug vampire-survivors \
  --max-pages 5000 --concurrency 6

Firecrawl's link map on a large MediaWiki is unreliable (a namespace-mixed, non-deterministic subset dominated by Templates/Modules), which is why sitemap discovery is preferred and automatic. Use --no-sitemap to force the link map.

Large and interrupted crawls

Capture is concurrent and streaming: each page is written to disk the moment it is scraped, and the pages/*.md files double as the resume ledger.

• --concurrency N — parallel page captures (default 5; raise for big sites, but a self-hosted Firecrawl has limited render workers).

• Resume is automatic: if a crawl is interrupted (pages written but pages.jsonl never finalized), re-running the same command captures only the pages still missing.

• --incremental — capture only URLs not already in the corpus. A cheap top-up for a large corpus after new pages are published; untouched pages keep their original crawled_at.

# Big wiki: more parallelism, resume-safe if it dies partway
uv run docs-to-mcp crawl https://example.com/wiki --slug example-wiki --max-pages 5000 --concurrency 10

# Later: pull in only newly published pages
uv run docs-to-mcp crawl https://example.com/wiki --slug example-wiki --max-pages 5000 --incremental

Layout

data/docs/<slug>/
  pages/<page_id>.md   # normalized markdown with frontmatter
  pages.jsonl          # per-page metadata + source URLs
  index.sqlite         # FTS5 search index
  manifest.json        # root_url + last_crawled_at, used by refresh_docs

MCP tools

The server exposes a small, discovery-friendly surface:

• search_docs(query, limit) — ranked page matches (BM25) with snippets.

• get_doc(page_id) — one captured page with metadata and full markdown.

• list_docs(section, limit) — list captured pages, optionally by section.

• refresh_docs(max_pages) — re-crawl the source and rebuild corpus + index.

Using the server from an MCP client

Register the captured corpus as a local stdio MCP server. Example OpenCode entry (one entry per slug; the same generic server is selected via --slug):

"opencode-docs": {
  "type": "local",
  "command": ["uv", "run", "--project", "docs-to-mcp",
              "docs-to-mcp", "serve", "--slug", "opencode-docs"],
  "environment": { "FIRECRAWL_API_URL": "http://localhost:3002" },
  "enabled": true
}

FIRECRAWL_API_URL is only needed by refresh_docs; plain search/read work offline.

Localized sites

By default a crawl captures the canonical (unprefixed) docs and drops localized paths like /docs/de/... or /docs/pt-br/..., detected via ISO 639-1 codes. Pass --locale de to capture a specific language instead. A site that is entirely localized falls back to keeping its localized pages rather than an empty corpus.

Search ranking

Results are ranked by BM25 with title, heading, and category matches weighted above body text. Because FTS5 normalizes relevance by whole-document length, only the lead (~6 KB) of each page's prose is indexed — otherwise a large page's title/heading match would be drowned out by its length. Headings are indexed from the full page, so structure stays searchable; only deep prose is excluded from search. Full page text is always available via get_doc.

A query first requires all terms (precise); if that returns nothing for a multi-word natural-language question, it falls back to matching any term and lets BM25 rank — so a query like "best weapons for Pasqualina" still returns results.

Known limitations

• Search matches title, headings, and the lead of each page — a term that appears only deep in a long page's prose may not surface; open the page with get_doc.

• Ranking is text-relevance only (no page-authority signal), so for an ambiguous one-word query several similarly-titled pages may outrank the canonical one.

• Category grouping reads MediaWiki Category pages: maintenance/tracking categories (e.g. "Pages needing…") appear alongside content ones, and a category whose page renders its members dynamically may come back empty.

• On sites larger than --max-pages, discovery keeps the alphabetically-first URLs; raise --max-pages to capture the full corpus.

• A real doc section named with a 2-letter ISO code (e.g. /docs/is) would be misread as a locale and dropped by default; use --locale to override.

• Link rewriting handles inline markdown links/images, not reference-style links.