Lumichess
Chess tools: game reviews for Chess.com and Lichess, FIDE ratings, openings, positions, puzzles.
Descripción del creador (original en inglés)
Read-only MCP server for Goodreads (no API required): search, books, shelves, ratings
Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.
Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.
Elige la app de IA que usas y copia la configuración.
GOODREADS_USER_IDNecesitas
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json
{
"mcpServers": {
"goodreads-ai": {
"command": "uvx",
"args": [
"goodreads-mcp-ai"
],
"env": {
"GOODREADS_USER_ID": "your-value"
}
}
}
}Ejecuta esto en tu terminal
claude mcp add goodreads-ai --env GOODREADS_USER_ID=your-value -- uvx goodreads-mcp-aiCompleta los valores vacíos antes de ejecutarlo.
Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"goodreads-ai": {
"command": "uvx",
"args": [
"goodreads-mcp-ai"
],
"env": {
"GOODREADS_USER_ID": "your-value"
}
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"goodreads-ai": {
"type": "stdio",
"command": "uvx",
"args": [
"goodreads-mcp-ai"
],
"env": {
"GOODREADS_USER_ID": "your-value"
}
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
GOODREADS_USER_ID envEsta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 10 nov 2026
El nombre en el registro se verificó con la cuenta de GitHub Danathar, dueña del repo
Todavía no hemos leído este repo en GitHub
Todavía no hemos leído este repo en GitHub
Este se ejecuta en tu computadora; no hay endpoint en internet
Se ejecuta en tu computadora y usa ajustes locales en su lugar
Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.
El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.
Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).
El repo de GitHub cambió por última vez –, y la última versión (2026.11.0) llegó al registro anteayer.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Python (uv) en tu computadora.
En la misma categoría: Lumichess, KILN, SEGAN
Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.
Cómo leer una tarjeta:↓ Descargas por semanai★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi
Chess tools: game reviews for Chess.com and Lichess, FIDE ratings, openings, positions, puzzles.
Smooth stylised 3D game models written as text: lint, preview and build glTF and Godot scenes.
Game sound effects and music written as text: lint, listen and build WAVs and Godot resources.
Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.
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.
Solo se muestra el principio; lee el resto en el repo