WilmAI
Finland's Wilma school system for parents and students: schedule, homework, exams and messages.
제작자가 쓴 설명(영어 원문)
Read-only MCP server for Goodreads (no API required): search, books, shelves, ratings
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
GOODREADS_USER_ID필요한 것
어떤 앱을 쓰세요?
위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요
{
"mcpServers": {
"goodreads-ai": {
"command": "uvx",
"args": [
"goodreads-mcp-ai"
],
"env": {
"GOODREADS_USER_ID": "your-value"
}
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add goodreads-ai --env GOODREADS_USER_ID=your-value -- uvx goodreads-mcp-ai실행하기 전에 빈 값을 채워 주세요.
프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"goodreads-ai": {
"command": "uvx",
"args": [
"goodreads-mcp-ai"
],
"env": {
"GOODREADS_USER_ID": "your-value"
}
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"goodreads-ai": {
"type": "stdio",
"command": "uvx",
"args": [
"goodreads-mcp-ai"
],
"env": {
"GOODREADS_USER_ID": "your-value"
}
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
GOODREADS_USER_ID env이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 9일 점검
레지스트리 이름이 GitHub 계정 Danathar 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요
내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요
내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.
내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(2026.11.0)은 어제 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Python (uv)도 있어야 해요.
같은 카테고리에는: WilmAI, Librus Synergia, KitchenWatt (France)
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:↓ 주간 다운로드i★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
Finland's Wilma school system for parents and students: schedule, homework, exams and messages.
Librus Synergia school gradebook: grades, attendance, timetable, homework, messages.
French food processor comparison: search, filter and compare KitchenWatt's catalogue.
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
Thank you, [Shreeya Chand](https://github.com/shreeyachand). This project is a fork of
shreeyachand/goodreads-mcp, and it would not exist without that work. The core idea of reaching Goodreads without its retired API, and the tool set this server offers, come from Shreeya's project. We're grateful for it, and for the MIT licence that let us build on it. If this server is useful to you, please go star the original.
A read-only MCP server for Goodreads — built without the Goodreads API, because there hasn't been one since December 2020. Lets an LLM find and research books, ratings, and reviews. Tools ride on the JSON autocomplete endpoint, the __NEXT_DATA__ blob embedded in book pages, the AppSync GraphQL backend the Goodreads website itself uses, and the shelf RSS feed, which Goodreads has answered with 404 for every user since Oct 2026. No login, no cookies, no writes — public data only.
| tool | source / what it returns |
|---|---|
search_books | JSON autocomplete endpoint (stable) — book_id, title, author, rating, cover; max_results defaults to 10, but the endpoint returns about 5 matches at most |
get_book | __NEXT_DATA__ via the .xml page (stable) — details, cover, ratings histogram, every series membership, review-language breakdown (review_language_limit, default 5, max 25); edition publication_date and the work's first_published, original_title, language, isbn / isbn13 / asin, author_url (each null when absent) |
get_reviews | GraphQL — paginated reader reviews (text, rating, likes, date, spoiler flag, permalink); limit default 10, capped at 100; server-side min_rating / max_rating (1–5, min ≤ max), sort (relevance default, newest, oldest), language (two-letter code, as in get_book's review_languages) and search over review text; exclude_spoilers; reports has_more |
similar_books | GraphQL — paginated "readers also enjoyed" recommendations; limit up to 100 |
author_books | GraphQL — paginated author bibliography, ranked by popularity, from any of their books, plus author_url; limit up to 100 |
series_books | GraphQL — paginated series books with reading-order placement; series_index (zero-based, in get_book's series_memberships order) picks the series; limit up to 100 |
get_editions | GraphQL — paginated editions (format, ISBN, publisher, publication date as publication_date YYYY-MM-DD plus raw publication_time in epoch ms); limit up to 100 |
book_lists | GraphQL — paginated Listopia lists a book appears on (title, votes, size); limit up to 100 |
popular_books | GraphQL — most popular books by release year, or a single month (1–12), ranked; limit capped at 50 |
compare_books | get_book for each id (__NEXT_DATA__ via .xml) — ranks 1–10 distinct books by rating with positive/critical share; a repeated id is fetched once; more than 10 ids is refused; a book that fails comes back as an error entry; if Goodreads answers 429 or 503 the comparison stops there, keeps the books already fetched, and marks the rest as not fetched |
get_shelf | shelf RSS feed — books on a public shelf (rating and year as ints, None when absent; dates as YYYY-MM-DD; plus cover, pages, my_review); page starts at 1, about 100 items per page; user_id overrides the configured user; raises LoginRequired for a private profile, and ShelfFeedUnavailable while Goodreads answers the feed with 404 (since Oct 2026) |
list_shelves | best-effort HTML scrape of the public profile page — shelf names; raises LoginRequired for a private profile |
Every tool is registered with MCP read-only annotations (read-only, non-destructive, idempotent).
The discovery tools all take a book_id and return results carrying book_id/title/author/rating/url, so an agent can chain them — e.g. similar_books → get_reviews on a recommendation. book_id is a string on both sides ("54493401", or the slug form "54493401-title" on input), so a value copied out of one result is accepted as-is by the next call. average_rating is a number (or null) in every tool, so results from different tools sort together. This is the structured book graph a general web search can't assemble.
The five paginated discovery tools (similar_books, author_books, series_books, get_editions, book_lists) page in batches of 20 and accept a total limit up to 100; popular_books caps limit at 50. Responses include returned and has_more, keeping larger lookups useful without allowing unbounded traffic.
WAF and login note: Goodreads book HTML pages now sit behind an AWS WAF
JavaScript challenge (HTTP 202) that plain HTTP clients can't solve.
get_book
routes around it via the
.xml-suffixed page, so it still works without a
browser. If Goodreads ever extends the WAF to a path we depend on, the client
raises
WAFChallengewith a clear message instead of a confusing parse error.
The review-list page (
/review/list/{uid}) became login-only in Sep 2026; the
client raises
LoginRequiredon a sign-in redirect for the same reason, and
list_shelvesreads the public profile page instead.
With pip:
cd goodreads-mcp
python3.11 -m venv .venv && .venv/bin/pip install -e .
Or with uv, which is also what the Claude Desktop bundle uses:
cd goodreads-mcp
uv sync uv run goodreads-mcp
Requires Python ≥ 3.11.
Each date-numbered release is also published to PyPI as goodreads-mcp-ai (the goodreads-mcp name there belongs to an unrelated project) and listed on the official MCP registry as io.github.Danathar/goodreads-mcp-ai, from server.json. The listing carries a uvx runtime hint; a client that follows it runs uvx goodreads-mcp-ai, which you can also run yourself.
No login or cookies — everything is public data. The only setting is your numeric user_id, the default for the shelf tools. It's the number in goodreads.com/user/show/ -yourname; you can also pass user_id to each shelf tool per call.
mkdir -p ~/.config/goodreads-mcp
cat > ~/.config/goodreads-mcp/config.json << 'EOF' { "user_id": "12345678" } EOF
Env var GOODREADS_USER_ID overrides the file. A config file that can't be read, isn't valid JSON, isn't a JSON object, or has a user_id that is neither a string nor a whole number is ignored with a warning on stderr (a bare number such as 12345678 is accepted and used as its string form; booleans, floats, lists and objects are ignored); the server still starts.
Bundle. Each release carries a goodreads-mcp.mcpb. Releases come out monthly when the server itself changed, numbered by date (2026.10.0, 2026.10.1, 2026.11.0); 0.1.1 was the last of the old numbering, and every date-numbered release is newer than it. See CONTRIBUTING.md for how one is cut. Open the .mcpb in Claude Desktop to install. The bundle ships no dependencies — the manifest launches the server with uv run, and the host resolves pyproject.toml into a private environment on first launch — so one bundle runs on macOS, Windows and Linux with any Python ≥ 3.11. The bundle's optional "Goodreads User ID" setting (user_config.goodreads_user_id) is passed to the server as GOODREADS_USER_ID; left empty, it is passed as an empty value, and the server falls back to the config file.
Manual. Add the server to claude_desktop_config.json — on macOS ~/Library/Application Support/Claude/claude_desktop_config.json, on Windows %APPDATA%\Claude\claude_desktop_config.json; in any version, Settings → Developer → Edit Config opens it:
{
"mcpServers": { "goodreads": { "command": "/path/to/goodreads-mcp/.venv/bin/goodreads-mcp" } } }
Or for development, mcp dev goodreads_mcp/server.py gives you the Inspector UI to poke each tool.
The endpoints are unofficial, so verify in this order:
search_books("project hail mary") — should just workget_book("54493401") — confirms the .xml/WAF workaround; check the histogram is populatedget_reviews("54493401") — should return real review textget_shelf("to-read") — raises ShelfFeedUnavailable while Goodreads answers the shelf feed with 404 (since Oct 2026); that is not a user_id problemlist_shelves() — best-effort shelf-name scrapeFor an end-to-end example that chains the tools, see prompts/research-a-book.md.
.venv/bin/pip install -e ".[test]" # pytest + pytest-cov
.venv/bin/pytest # offline parser/unit tests GOODREADS_LIVE=1 .venv/bin/pytest # + live network smoke tests
The offline suite runs on fixtures; CI runs it with pytest-cov and enforces a coverage floor (--cov-fail-under in ci.yml). The live smoke tests are in tests/e2e/test_smoke_live.py and skip unless GOODREADS_LIVE=1 is set. The nightly compliance run runs the live suite against the real endpoints every night, so upstream drift shows up within a day.
앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요