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simentob

TMDB MCP Server

by simentob

tmdb_search_movies

Search movies by title or keyword with TMDB API. Refine results using year, region, language, and adult content filters. Returns formatted movie details.

Instructions

Search for movies using the TMDB API.
Args:
    query: The text query to search for (required).
    include_adult: Whether to include adult content (default: False).
    language: Language code (default: en-US).
    page: Page of results (default: 1).
    primary_release_year: Filter by release year (optional).
    region: Filter by region (optional).
    year: Filter by year (optional).
Returns:
    Formatted string with movie search results.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNo
yearNo
queryYes
regionNo
languageNoen-US
include_adultNo
primary_release_yearNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations provided, the description must carry the full burden. It does disclose the return format ('Formatted string with movie search results'), which is useful. However, it does not explicitly state that this is a read-only operation or mention any side effects, permissions, or error behavior, which is a gap given the absence of annotations.

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 concise and front-loaded with the primary purpose. The Args and Returns sections are logically structured, but it redundantly repeats defaults already present in the schema. Still, everything included serves a purpose, and it remains reasonably compact.

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

Completeness3/5

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

The description covers all parameters and states the return type, but it lacks usage context such as when to choose this tool over siblings. Given the output schema exists, the minimal return description is acceptable, but the overall guidance is incomplete for an agent deciding which search tool to use.

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?

The input schema has no descriptions for parameters (0% coverage), so the description's parameter explanations are essential. It lists all 7 parameters with brief meanings (e.g., 'query: The text query to search for (required)'). However, it does not clarify the distinction between 'primary_release_year' and 'year', and lacks format details for fields like language and region, so it is not fully comprehensive.

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

Purpose4/5

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

The description clearly states the action ('Search for movies') and the resource (TMDB API), making the core purpose unambiguous. However, it does not explicitly distinguish itself from sibling tools like tmdb_multi_search or tmdb_trending_search, so it lacks explicit sibling differentiation.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives such as tmdb_multi_search or tmdb_trending_search. It only describes what the tool does without any context on selection criteria or exclusions.

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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