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Grinv

TMDB MCP Server

Get similar titles

get_similar
Read-only

Discover movies or TV shows related by shared genres and keywords, filtering out broad matches to focus on thematically adjacent titles. Use this to find genre- or keyword-linked content.

Instructions

Get titles TMDB considers similar to a given movie or TV show, based on shared genres and keywords — a blunter heuristic than get_movie_recommendations'/get_tv_recommendations' behavioral (co-viewing) data, so results can still be thematically noisy (matching on a shared keyword despite an unrelated tone or plot). Results sharing only the source title's broadest genre (e.g. two titles that are both merely tagged 'Drama' among several genres) are filtered out per page, since a title with a common genre can otherwise return results spanning TMDB's entire catalog; a page can come back thin or empty for a niche title once that filter applies. total_pages/total_results reflect TMDB's raw, pre-filter counts, not how many titles actually pass this filter — don't use them to gauge how many genuinely similar titles exist or when to stop paging; expect per-page result counts well under 20, sometimes 0 (verified live: a real title returned total_pages:23897/total_results:477939 but only 3-6 actual results per page). Try recommendations first for thematically closer picks; use this when you specifically want genre/keyword-adjacent titles. Get the id from search_movies/search_tv.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesTMDB numeric id.
pageNo1-based page number for pagination (TMDB returns up to 20 results per page, max 500).
media_typeYesMedia type: 'movie' or 'tv'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageYes
resultsYes
total_pagesYes
total_resultsYes
Behavior5/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true. Description adds significant behavioral context: noise from shared keywords, filtering out broad genre matches, misleading total_pages/total_results counts, and per-page results may be thin or empty. No contradictions with 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?

Description is front-loaded with purpose, but is somewhat verbose. However, every sentence adds value (contrast with recommendations, caveats about filtering and pagination). Could be slightly more concise, but the information density justifies the length.

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 complexity (filtering, misleading pagination counts, possibility of empty results) and presence of output schema, the description fully covers behavioral nuances and return value interpretation. It explains not to use total_pages/total_results for gauging similarity depth, which is critical. Very complete.

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

Parameters3/5

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

Schema coverage is 100% with all parameters described. Description adds minor context (e.g., 'Get the id from search_movies/search_tv' for id, pagination context for page), but does not significantly enhance parameter meaning beyond the schema. Baseline of 3 is appropriate given high coverage.

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?

Description clearly states the tool retrieves titles TMDB considers similar based on shared genres and keywords, and explicitly contrasts with get_movie_recommendations/get_tv_recommendations. It uses specific verb 'Get' and specifies resource 'titles similar to a given movie or TV show', distinguishing it from siblings.

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?

Explicitly advises to use recommendations first for thematically closer picks, and use this for genre/keyword-adjacent similarity. Also states prerequisite: 'Get the id from search_movies/search_tv.' Provides clear when-to-use and when-not-to-use guidance.

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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