Kerbside F1
Free Formula 1 stats: standings, title maths, teammate battles and records, each linked to source.
Beschreibung vom Entwickler (englisches Original)
Read-only MCP server for Goodreads (no API required): search, books, shelves, ratings
Diese drei werden nie zu einer Punktzahl zusammengefasst: Meinungen, Messungen und Beliebtheit sind verschiedene Dinge.
Für diesen Server haben wir noch keine Zusammenfassung in einfacher Sprache geschrieben. Unten stehen die Fakten, die wir aus der Registry gelesen haben.
Wähle die KI-App, die du nutzt, und kopiere die Einstellungen.
GOODREADS_USER_IDDu brauchst
Welche App nutzt du?
Wähle oben deine App. Wir merken sie uns, damit der Button beim nächsten Mal „In … installieren“ anzeigt.
Öffne Claude Desktop → Settings → Developer → Edit Config und füge dies in claude_desktop_config.json ein
{
"mcpServers": {
"goodreads-ai": {
"command": "uvx",
"args": [
"goodreads-mcp-ai"
],
"env": {
"GOODREADS_USER_ID": "your-value"
}
}
}
}Führe das in deinem Terminal aus
claude mcp add goodreads-ai --env GOODREADS_USER_ID=your-value -- uvx goodreads-mcp-aiTrag die leeren Werte ein, bevor du ihn startest.
In .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"goodreads-ai": {
"command": "uvx",
"args": [
"goodreads-mcp-ai"
],
"env": {
"GOODREADS_USER_ID": "your-value"
}
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
In .vscode/mcp.json in deinem Projekt einfügen
{
"servers": {
"goodreads-ai": {
"type": "stdio",
"command": "uvx",
"args": [
"goodreads-mcp-ai"
],
"env": {
"GOODREADS_USER_ID": "your-value"
}
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
Dieser Server läuft auf deinem Computer, und ChatGPT kann sich nur mit Servern im Internet verbinden. Wähle eine andere App.
Deine Auswahl bleibt in einem Cookie auf diesem Gerät; kein Konto nötig.
GOODREADS_USER_ID envDiese Einstellungen werden aus dem Registry-Eintrag des Entwicklers erzeugt. Wenn in der README etwas anderes steht, folge der README.
Läuft auf deinem Computer; die Tool-Liste erscheint nach der Installation in deiner KI-App.
Wir prüfen Informationen, die jeder sehen kann; den Code haben wir nicht gelesen. Checks, die nicht zutreffen, werden nicht mitgezählt. · Geprüft am 8. Nov. 2026
Der Registry-Name wurde mit dem GitHub-Konto Danathar bestätigt, dem das Repo gehört
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieser Server läuft auf deinem Computer; es gibt keinen Endpunkt im Internet
Läuft auf deinem Computer und nutzt stattdessen lokale Einstellungen
Läuft auf deinem Computer; Tool-Listen können wir nur von Endpunkten im Internet lesen
Verglichen mit allen aktiven Servern dieser Kategorie in der Registry. Sterne zählen nur, wenn dem Herausgeber das Repo gehört.
Er hat 1 von 1 zutreffenden Basischecks bestanden. Den Code haben wir nicht gelesen. Bevor du einen Schlüssel oder Token hinzufügst, sieh nach, welche Zugriffe der Server verlangt, und nutze einen Schlüssel, der nur die Berechtigungen hat, die du brauchst.
Die Registry nennt keinen erforderlichen Schlüssel, manche Server bitten dich aber bei der ersten Nutzung, dich anzumelden.
Er läuft auf deinem Computer. Deine KI-App startet ihn bei Bedarf, und die Daten laufen nicht über die Server des Entwicklers (außer der Server ruft selbst einen Onlinedienst auf).
Das GitHub-Repo wurde zuletzt – geändert, und die neueste Version (2026.11.0) kam gestern in die Registry.
Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code. Außerdem brauchst du Python (uv) auf deinem Computer.
In derselben Kategorie: Kerbside F1, Open Food Facts MCP, Modrinth MCP
Server derselben Kategorie von anderen Entwicklern, sortiert nach Sternen.
So liest du eine Karte:↓ Downloads pro Wochei★ GitHub-SterneiDrei Säulen: Bewertungen · Checks · NutzungiAktualisiert = neueste Version in der Registry veröffentlichti
Free Formula 1 stats: standings, title maths, teammate battles and records, each linked to source.
MCP server for Open Food Facts product, ingredient, allergen, and nutrition lookup.
Search Minecraft mods on Modrinth, filter by loader/game version, browse versions.
Unverändert aus dem Repo des Entwicklers übernommen. PopNx MCP hat den Inhalt weder geprüft noch empfiehlt es ihn. HTML, Bilder und Skripte wurden entfernt. Links führen von dieser Website weg.
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.
Nur der Anfang wird gezeigt; den Rest findest du im Repo