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drakonkat

wizzy-mcp-tmdb

movie_credits

Fetch cast and crew credits for any movie using its TMDB ID to support movie analysis and AI-powered recommendations.

Instructions

Fetches cast and crew credits for a movie. Input: movie_id (required TMDB ID), language (optional ISO 639-1 code). Output: JSON with cast and crew details. Purpose: Retrieve detailed personnel information for movie analysis and recommendations by AI agents.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
languageNoISO 639-1 code (e.g., en-US)
movie_idYesTMDB Movie ID

Implementation Reference

  • The handler function executes the core logic of the movie_credits tool by calling the TMDB API endpoint for movie credits and returning the JSON-formatted response.
    handler: async ({movie_id, language}) => {
        const data = await tmdbFetch(`/movie/${movie_id}/credits`, {language});
        return {content: [{type: "text", text: JSON.stringify(data, null, 2)}]};
    }
  • The inputSchema defines the expected parameters for the movie_credits tool: required movie_id (number) and optional language (string).
    inputSchema: {
        type: "object",
        properties: {
            movie_id: {type: "number", description: "TMDB Movie ID"},
            language: {type: "string", description: "ISO 639-1 code (e.g., en-US)"}
        },
        required: ["movie_id"],
        additionalProperties: false
    },
  • The complete tool registration object for movie_credits in the tools array, binding name, description, schema, and handler for MCP server registration.
    {
        name: "movie_credits",
        description: "Fetches cast and crew credits for a movie. Input: movie_id (required TMDB ID), language (optional ISO 639-1 code). Output: JSON with cast and crew details. Purpose: Retrieve detailed personnel information for movie analysis and recommendations by AI agents.",
        inputSchema: {
            type: "object",
            properties: {
                movie_id: {type: "number", description: "TMDB Movie ID"},
                language: {type: "string", description: "ISO 639-1 code (e.g., en-US)"}
            },
            required: ["movie_id"],
            additionalProperties: false
        },
        handler: async ({movie_id, language}) => {
            const data = await tmdbFetch(`/movie/${movie_id}/credits`, {language});
            return {content: [{type: "text", text: JSON.stringify(data, null, 2)}]};
        }
    },
  • The tmdbFetch helper function used by the movie_credits handler to make authenticated API requests to TMDB.
    async function tmdbFetch(path, params = {}) {
        if (!TMDB_AUTH_TOKEN) {
            throw new Error("TMDB authorization token is not configured");
        }
        const url = new URL(TMDB_BASE + path);
        Object.entries(params).forEach(([k, v]) => {
            if (v !== undefined && v !== null && v !== "") url.searchParams.set(k, String(v));
        });
    
        const res = await fetch(url, {
            headers: {
                Accept: "application/json",
                Authorization: TMDB_AUTH_TOKEN,
            },
        });
        if (!res.ok) {
            const text = await res.text().catch(() => "");
            throw new Error(`TMDB request failed ${res.status}: ${text}`);
        }
        return res.json();
    }
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the output format ('JSON with cast and crew details') and hints at a read-only operation ('fetches', 'retrieve'), but lacks details on rate limits, authentication needs, error handling, or pagination. This provides basic context but misses key operational traits for robust agent use.

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 front-loaded with the core functionality and efficiently structured into input, output, and purpose sections in three sentences. However, the purpose sentence could be more concise (e.g., 'for AI-driven movie analysis and recommendations'), and some redundancy exists with schema details, slightly reducing efficiency.

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

Completeness4/5

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

Given the tool's low complexity (2 parameters, no nested objects) and lack of output schema, the description is reasonably complete—it covers what the tool does, input/output formats, and usage context. However, it could improve by addressing missing behavioral aspects like rate limits or error cases, which would enhance agent reliability in this 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?

Schema description coverage is 100%, so the schema already documents both parameters (movie_id as TMDB ID, language as ISO 639-1 code). The description adds minimal value by restating these in the input note, without providing additional semantics like format examples or usage tips beyond what the schema offers, meeting the baseline for 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?

The description clearly states the tool's purpose with specific verbs ('fetches', 'retrieve') and resources ('cast and crew credits', 'detailed personnel information'). It distinguishes from siblings like movie_images (visual content) and movie_reviews (critiques) by focusing on personnel data, making it easy for an AI agent to identify when to use this tool.

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

Usage Guidelines4/5

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

The description provides clear context for usage ('for movie analysis and recommendations by AI agents'), indicating this tool is for obtaining personnel data to support analytical tasks. However, it does not explicitly state when not to use it or name specific alternatives among siblings (e.g., person_details for individual details), leaving some ambiguity in tool selection.

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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