ao3-mcp

<!-- mcp-name: io.github.ArturLys/ao3-mcp -->

An MCP (Model Context Protocol) server that connects AI agents — Claude, Cursor, or any MCP client — to the Archive of Our Own. Search AO3 fanfiction with full filters, resolve fuzzy wording to canonical tags, and get fics actually read before they're recommended.

The trick: your agent never reads fic text. It delegates reading to a cheap secondary model (Gemini), which digests whole fics — even 150k-word novels — and returns structured reports. Your agent's context stays clean; the recommendations are based on the real text, not the blurb.

agent ──MCP──> server.py
                 ├─ ao3.py     AO3 scraping (no public API exists) — throttled and polite
                 └─ reader.py  Gemini reads the fics, reports back: plot, style,
                               prose samples, content notes, a ranking

Why this beats blurb-based recommendations

An AO3 blurb is an ad written by the author. This server's workflow is: search wide (40–60 results), have the reader model read the shortlist — up to 20 full fics in one call — and recommend only what was actually read, with verbatim prose samples so quality is judged from the text itself.

Not just for finding your next read

If you write with an AI — fanfic, original fiction, roleplay — this doubles as an inspiration engine. Mid-scene, your agent can pull up how real fic authors handle the exact beat you're on:

Find three highly-kudosed fics where rivals are forced to share a bed, read them,
and tell me how each one builds the tension — pacing, POV, what they leave unsaid.

The reader reports back with structure, style notes, and verbatim prose samples, so the model gets grounded in how the trope is actually written — not what it imagines fanfic sounds like. Works the same for roleplay: pull reports on fics that nail a character's voice and feed them in as style reference.

Install

Requires Python 3.10+ and a free Gemini API key:

Go to aistudio.google.com/api-keys, sign in with any Google account, and click "Create API key". The free tier is enough — no billing setup needed.

pip install ao3-mcp

Add to your agent

Point command at ao3-mcp and pass your key with --api-key:

{
  "mcpServers": {
    "ao3": {
      "command": "ao3-mcp",
      "args": ["--api-key", "YOUR_GEMINI_KEY"]
    }
  }
}

Prefer to keep the key out of the args list? Drop --api-key and pass it in an env block instead — the server reads GEMINI_API_KEY from the environment as a fallback:

"env": { "GEMINI_API_KEY": "YOUR_GEMINI_KEY" }

<details> <summary>Claude Code</summary>

claude mcp add ao3 -- ao3-mcp --api-key YOUR_GEMINI_KEY

</details> <details> <summary>Cursor</summary>

Cursor Settings → MCP → New MCP Server, paste the JSON config above.

</details> <details> <summary>Google Antigravity</summary>

Add the JSON config above to .gemini/antigravity/mcp_config.json.

</details> <details> <summary>VS Code / Copilot</summary>

code --add-mcp '{"name":"ao3","command":"ao3-mcp","args":["--api-key","YOUR_GEMINI_KEY"]}'

</details>

Then just ask:

Find me a completed enemies-to-lovers longfic in <fandom>, read the top candidates, and tell me which is best written.

Launch params

| Param | Env var | Default | What it does | | ----------------- | --------------------- | -------------------- | --------------------------------------------- | | --api-key | GEMINI_API_KEY | — | Gemini API key (required). | | --model | GEMINI_MODEL | gemini-flash-latest| Model the reader uses. | | --backup-model | GEMINI_MODEL_BACKUP | gemini-flash-lite-latest | Fallback model when the main one is throttled. | | --min-interval | AO3_MIN_INTERVAL | 0.6 | Minimum seconds between AO3 requests. |

Tools

| Tool | What it does | | -------------- | ---------------------------------------------------------------------------- | | search_works | Search AO3: fandom, ship, character, tags, rating, word count, completion, sorting. 20 results/page, up to 5 pages per call. The query field supports AO3's full search-operator syntax (words>10000, kudos>500, sort:kudos, …). | | find_tags | Live autocomplete — fuzzy wording → canonical AO3 tag, fandom, ship, or character names. | | get_work | Full metadata card for one work: tags, stats, summary, series info. | | read_works | Reads 1–20 full fics with the secondary model and returns a structured report per fic — plot, characters, style, verbatim prose samples, content notes — plus a comparison ranking them against your question. |

Fic downloads are cached locally for 24h, so re-reading a fic with a new question costs no AO3 requests.

Good to know

• AO3 has no API — this scrapes its (clean) HTML, one request at a time, throttled to one every 0.6s by default (tune with --min-interval) and honoring Retry-After. AO3 is volunteer-run; the politeness is deliberate.
• Cloudflare: AO3 blocks plain HTTP clients. This uses curl_cffi with a mobile-Safari TLS fingerprint, which passes as of writing. If requests start failing with 403 + cf-mitigated: challenge, change IMPERSONATE in ao3.py.
• Privacy: fic text goes to Google's Gemini API for reading; nothing else leaves your machine, no telemetry.
• Adult content: AO3 hosts works across all ratings. The server passes through whatever your search scopes — use the rating filter and AO3's warning tags to control what gets fetched.

Make it yours

It's a small, single-purpose server — a few hundred readable lines with no framework magic. Fork it and edit anything: rewrite the reader's prompt, swap in a different model, change the throttle, add a tool. That's the intended way to use it.

Run it from source:

git clone https://github.com/ArturLys/ao3-mcp.git
cd ao3-mcp
python -m venv .venv
source .venv/bin/activate        # Windows: .venv\Scripts\activate
pip install -r requirements.txt

python smoke_test.py YOUR_GEMINI_KEY   # end-to-end check: search → download → digest
python server.py --api-key YOUR_GEMINI_KEY   # or point your client's command at this

Credits

• AO3 access approach builds on ao3_api by wendytg.
• All fanworks belong to their authors on the Archive of Our Own, a project of the Organization for Transformative Works.

License

MIT