Ao3 Mcp

io.github.ArturLys由 GitHub 账号 ArturLys 发布v0.1.2更新于 Oct 27, 2026

Search AO3 and have fics read by a secondary model before they're recommended

概览

AI 生成的概览

搜索 AO3 同人小说,并由一个次级 Gemini 模型通读全文,向助手返回结构化报告与排名。

可以做什么
  • 按同人圈、配对、角色、标签、分级、字数、完结状态和排序搜索 AO3 作品,支持 AO3 搜索运算符语法。
  • 通过实时自动补全把模糊说法解析为规范的 AO3 标签、同人圈、配对和角色名。
  • 获取单篇作品的元数据卡片,包括标签、统计、摘要和系列信息。
  • 用次级 Gemini 模型通读 1 至 20 篇全文,返回每篇的情节、角色、文风、原文片段和内容提示报告,并给出对比排名。
适用场景
适合希望推荐基于正文而非作者简介的场景,也适合用 AI 写小说或角色扮演时,从真实同人作品中获取某一题材的节奏与文风参考。
运行要求
以 Python 3.10 以上版本通过 pip 安装的本地包运行。需要 Gemini API 密钥,可通过 --api-key 参数或 GEMINI_API_KEY 环境变量提供,说明称免费额度已足够。可选设置用于选择阅读模型、备用模型和 AO3 请求间隔。需要访问 AO3 和 Google Gemini API 的网络连接。
安装前请注意
小说正文会发送给 Google 的 Gemini API 阅读,所搜索的内容会离开本机。密钥可放在环境变量而非命令行参数中,以免出现在参数列表里。AO3 没有官方 API,本服务通过抓取网页获取内容,可能被 Cloudflare 验证拦截。AO3 包含各种分级作品,包括成人内容,会按搜索过滤条件原样获取。小说下载内容会在本地缓存 24 小时。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Ao3 Mcp,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。

其他 MCP 客户端

参照 仓库 中的启动说明。

README

ao3-mcp

[PyPI] [CI] [Python 3.10+] [License: MIT]

[ao3-mcp MCP server]

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.

bash
pip install ao3-mcp

Add to your agent

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

json
{  "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:

json
"env": { "GEMINI_API_KEY": "YOUR_GEMINI_KEY" }
Claude Code
bash
claude mcp add ao3 -- ao3-mcp --api-key YOUR_GEMINI_KEY
Cursor

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

Google Antigravity

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

VS Code / Copilot
bash
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.

Launch params

ParamEnv varDefaultWhat it does
--api-keyGEMINI_API_KEY—Gemini API key (required).
--modelGEMINI_MODELgemini-flash-latestModel the reader uses.
--backup-modelGEMINI_MODEL_BACKUPgemini-flash-lite-latestFallback model when the main one is throttled.
--min-intervalAO3_MIN_INTERVAL0.6Minimum seconds between AO3 requests.

Tools

ToolWhat it does
search_worksSearch 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_tagsLive autocomplete — fuzzy wording → canonical AO3 tag, fandom, ship, or character names.
get_workFull metadata card for one work: tags, stats, summary, series info.
read_worksReads 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:

bash
git clone https://github.com/ArturLys/ao3-mcp.gitcd ao3-mcppython -m venv .venvsource .venv/bin/activate        # Windows: .venv\Scripts\activatepip install -r requirements.txt
python smoke_test.py YOUR_GEMINI_KEY   # end-to-end check: search → download → digestpython server.py --api-key YOUR_GEMINI_KEY   # or point your client's command at this

Credits

License

MIT

来源:README.md,提交 3e596e5

工具

0
SourceWeft 尚未在沙箱中启动过该软件包,因此其工具未知。

版本历史

1
  1. v0.1.2最新Sep 16, 2026

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