F5 AI Security Docs MCP Server

An MCP server that provides search and fetch tools over the entire F5 AI Security documentation site, modeled after the Strands Agents MCP server.

It exposes two tools to MCP-compatible AI assistants:

• search_docs – TF-IDF, Markdown-aware ranked search across all docs pages.

• fetch_doc – Token-efficient reader: page catalog → table of contents → individual sections.

How it enumerates the whole site

docs.aisecurity.f5.com is a VitePress static site. VitePress publishes a complete page manifest at /hashmap.json whose keys are the markdown source paths for every rendered page. The server downloads that file once at startup and derives a URL for each page:

api-docs_first-steps.md           -> /api-docs/first-steps.html
release-notes_2026-06-17-saas.md  -> /release-notes/2026-06-17-saas.html
operations_get_scans.md           -> /operations/get_scans.html
index.md                          -> /index.html

(First _ becomes the section separator; .md becomes .html.)

This captures all ~219 pages — system-overview/, get-started/, application-docs/, api-docs/, api-reference/, the full REST operations/ reference, integrations/, red-team/, use-cases/, reference/, glossary, and release-notes/ — with no fragile HTML crawling.

Related MCP server: FastMCP Documentation Search Server

Architecture

src/f5_aisec_mcp_server/
  config.py              # base_url, allowed host, hashmap path, timeouts
  server.py              # search_docs + fetch_doc MCP tools
  utils/
    page_source.py       # download /hashmap.json -> [(title, url), ...]
    cache.py             # global index + lazy page hydration
    doc_fetcher.py       # HTTP fetch + VitePress HTML -> clean text
    indexer.py           # lightweight TF-IDF inverted index (stdlib only)
    text_processor.py    # titles, snippets, TOC/section parsing

Flow: at startup the hashmap is parsed and the TF-IDF index is seeded with page titles only (no content fetched → fast start). search_docs ranks by title, then lazily fetches the top results to build content snippets. fetch_doc lets the model browse a page's TOC and pull just the section it needs.

Install / Run

Requires Python 3.13+ (developed and tested on 3.13.14).

cd f5-aisec-mcp
python3.13 -m venv venv && source venv/bin/activate
pip install -e ".[dev]"

# Quick-test with the MCP Inspector
npx @modelcontextprotocol/inspector python -m f5_aisec_mcp_server

MCP client configuration

Point command at the venv's Python (absolute path) so the right interpreter and installed mcp package are used:

{
  "mcpServers": {
    "f5-aisec": {
      "command": "/Users/<<USER>>/path/to/project/f5-aisec-mcp/venv/bin/python",
      "args": ["-m", "f5_aisec_mcp_server"],
      "env": { "FASTMCP_LOG_LEVEL": "INFO" },
      "disabled": false,
      "autoApprove": ["search_docs", "fetch_doc"]
    }
  }
}

Tests

pytest tests/