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AmeliaMiddleton

moviefinder-mcp

get_recommendations

Retrieve movie recommendations based on a given TMDB movie ID. Find similar titles users enjoyed.

Instructions

Get TMDB's recommendations for users who liked the given movie.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
movie_idYesTMDB movie ID to base recommendations on.

Implementation Reference

  • The handler function that calls TMDB /movie/{id}/recommendations API and returns recommendations mapped through summarizeMovie.
    export async function getRecommendations(args: { movie_id: number }) {
      const data = await tmdbGet<PaginatedResponse<MovieListItem>>(
        `/movie/${args.movie_id}/recommendations`,
        { language: "en-US" },
      );
      return {
        total_results: data.total_results,
        results: data.results.map(summarizeMovie),
      };
    }
  • Zod schema defining input validation: requires a positive integer movie_id.
    export const getRecommendationsSchema = {
      movie_id: z.number().int().positive().describe("TMDB movie ID to base recommendations on."),
    };
  • src/index.ts:81-86 (registration)
    Registers the tool 'get_recommendations' with the MCP server, wiring the schema and handler (wrapped for error handling).
    server.tool(
      "get_recommendations",
      "Get TMDB's recommendations for users who liked the given movie.",
      getRecommendationsSchema,
      wrap(getRecommendations),
    );
  • Helper used by getRecommendations to map raw movie list items into a summarized response shape.
    function summarizeMovie(m: MovieListItem) {
      return {
        id: m.id,
        title: m.title,
        year: yearOf(m.release_date),
        overview: m.overview ?? null,
        rating: m.vote_average ?? null,
        poster: posterUrl(m.poster_path),
      };
    }
Behavior2/5

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

No annotations are provided, and the description does not disclose behavioral traits such as whether the operation is read-only, any rate limits, or what happens if the movie ID is invalid. The description solely states the action without revealing underlying behavior.

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

Conciseness5/5

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

The description is a single sentence of 12 words, which is highly efficient and front-loaded. Every word is essential, and there is no redundancy.

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?

Given the tool has only one parameter and no output schema, the description is functional but lacks details about the return format, pagination, or limitations. It is minimally complete but could be improved with additional context.

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?

The input schema has 100% description coverage for the single parameter 'movie_id', so the schema already explains the parameter. The description adds no additional semantic value beyond the schema, achieving the baseline score.

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 verb 'Get' and the resource 'TMDB's recommendations for users who liked the given movie', making the purpose unambiguous. However, it does not differentiate from the sibling tool 'get_similar', which likely serves a similar function, so the purpose clarity is slightly diminished.

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 like 'get_similar' or 'discover_movies'. No usage context or exclusions are mentioned.

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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