Search AO3 and have fics read by a secondary model before they're recommended
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io.github.ArturLys/ao3-mcp is an MCP server that search AO3 and have fics read by a secondary model before they're recommended. Its tool list has not been published yet over stdio, sse and http, requires no API key, and scores 85/100 on MCPpedia's security, maintenance and efficiency rubric.
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"ao3": {
"args": [
"--api-key",
"YOUR_GEMINI_KEY"
],
"command": "ao3-mcp"
}
}
}Are you the author?
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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.
Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.
uvx 'ao3-mcp' 2>&1 | head -1 && echo "✓ Server started successfully"
After testing, let us know if it worked:
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No known CVEs.
Checked ao3-mcp against OSV.dev.
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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
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.
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.
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
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" }
claude mcp add ao3 -- ao3-mcp --api-key YOUR_GEMINI_KEY
Cursor Settings → MCP → New MCP Server, paste the JSON config above.
Add the JSON config above to .gemini/antigravity/mcp_config.json.
code --add-mcp '{"name":"ao3","command":"ao3-mcp","args":["--api-key","YOUR_GEMINI_KEY"]}'
Then just ask:
Find me a completed enemies-to-lovers longfic in <fandom>, read the top candidates, and tell me which is best written.
| Param | Env var