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Grinv

TMDB MCP Server

Get compact movie card(s)

get_movies
Read-only

Fetch compact movie cards with title, year, genres, and vote average for 1-20 TMDB IDs in one call. Optionally include IMDb, Rotten Tomatoes, and Metacritic ratings.

Instructions

Get a compact card — title, year, genres, vote average, and (opt-in) ratings — for 1-20 movies by TMDB id in one call. Deliberately trimmed (no overview, cast, budget, certifications, production companies, etc.): use this for a single id too when you only need that headline info and not the full get_movie payload, not just for checking many at once. Call get_movie instead when you need the full details for a title. A bad/unknown id never fails the whole call — that entry comes back {id, found:false, reason} instead, in the same order as ids.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idsYesTMDB movie ids to fetch (1-20). Get them from search_movies/discover_movies/get_similar/get_movie_recommendations/etc.
regionNoISO-3166-1 country code for region-specific results (default 'US').
languageNoOverride the response language (ISO-639-1, optionally with a region), e.g. 'ru-RU' or 'en-US'. Localizes titles/overviews/genre names. Defaults to the server's TMDB_LANGUAGE.
include_ratingsNoIf true, enrich every card with compact IMDb/Rotten Tomatoes/Metacritic ratings from OMDb (requires OMDB_API_KEY) — one extra OMDb lookup per id, so a large batch means a burst of OMDb calls; mind OMDb's own rate limit. Unlike get_movie/get_tv, defaults to false (off) here.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultsYes
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the annotations (readOnlyHint, openWorldHint), the description discloses that bad IDs return a structured error entry rather than failing the entire call, and that include_ratings triggers OMDb lookups with rate limit considerations. This adds significant behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and front-loaded with the core purpose. However, there is some redundancy (e.g., mentioning 'for a single id too' twice). Still, it is efficient and each sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 4 parameters (1 required), an output schema is present, and annotations exist, the description covers all necessary aspects: usage scope, error behavior, default values, rate limit warnings, and alternatives. No gaps are apparent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the region and language parameters (ISO codes), the include_ratings default (false) and OMDb dependency, and where ids can be obtained (e.g., search_movies). This extra context earns a 4.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it retrieves compact movie cards (title, year, genres, vote average, optional ratings) for 1-20 movies by TMDB ID. It explicitly distinguishes from get_movie by noting it does not include full details like overview, cast, etc. This makes the tool's specific purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit when-to-use guidance: use for single or batch calls when only headline info is needed, and call get_movie for full details. It also explains error handling for bad IDs, which helps the agent decide when to use this tool vs alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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