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drakonkat

wizzy-mcp-tmdb

tv_credits

Fetch cast and crew credits for TV shows using TMDB ID to analyze personnel information and support AI-driven recommendations.

Instructions

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
languageNoISO 639-1 code (e.g., en-US)
tv_idYesTMDB TV Show ID

Implementation Reference

  • The handler function for the 'tv_credits' tool. It fetches cast and crew credits for a given TV show ID from the TMDB API using the tmdbFetch helper and returns the JSON data as text content.
    handler: async ({tv_id, language}) => {
        const data = await tmdbFetch(`/tv/${tv_id}/credits`, {language});
        return {content: [{type: "text", text: JSON.stringify(data, null, 2)}]};
    }
  • The input schema for the 'tv_credits' tool, defining required 'tv_id' (number) and optional 'language' (string).
    inputSchema: {
        type: "object",
        properties: {
            tv_id: {type: "number", description: "TMDB TV Show ID"},
            language: {type: "string", description: "ISO 639-1 code (e.g., en-US)"}
        },
        required: ["tv_id"],
        additionalProperties: false
    },
  • The complete 'tv_credits' tool definition object in the tools array, which is used by the MCP server to register and handle tool calls.
    {
        name: "tv_credits",
        description: "Fetches cast and crew credits for a TV show. Input: tv_id (required TMDB ID), language (optional ISO 639-1). Output: JSON with cast and crew details. Purpose: Retrieve detailed personnel information for TV show analysis and recommendations by AI agents.",
        inputSchema: {
            type: "object",
            properties: {
                tv_id: {type: "number", description: "TMDB TV Show ID"},
                language: {type: "string", description: "ISO 639-1 code (e.g., en-US)"}
            },
            required: ["tv_id"],
            additionalProperties: false
        },
        handler: async ({tv_id, language}) => {
            const data = await tmdbFetch(`/tv/${tv_id}/credits`, {language});
            return {content: [{type: "text", text: JSON.stringify(data, null, 2)}]};
        }
    },
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It states the tool fetches data and outputs JSON, but does not disclose behavioral traits such as rate limits, authentication needs, error handling, or whether it's a read-only operation (implied by 'fetches' but not explicit). For a tool with no annotations, this leaves significant gaps in understanding its behavior.

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 appropriately sized with three sentences that are front-loaded with the core functionality. However, the last sentence ('Purpose: Retrieve detailed personnel information...') could be integrated more smoothly, and some redundancy exists (e.g., restating input/output details). Overall, it's efficient but not perfectly structured.

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 no annotations, no output schema, and 100% schema coverage, the description is moderately complete. It covers the basic purpose and parameters but lacks details on behavioral aspects (e.g., rate limits, errors) and output structure beyond 'JSON with cast and crew details'. For a tool with no structured output, more context on the return format would be beneficial.

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 (tv_id as TMDB TV Show ID and language as ISO 639-1 code). The description adds minimal value by restating that tv_id is required and language is optional, but does not provide additional meaning beyond what the schema offers, such as examples of language codes or details on tv_id sourcing.

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 specific action ('Fetches cast and crew credits'), resource ('for a TV show'), and distinguishes it from siblings like 'movie_credits' (for movies) and 'person_details' (for individuals). It explicitly mentions the purpose for AI agents, making it highly specific and differentiated.

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

Usage Guidelines3/5

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

The description implies usage for 'TV show analysis and recommendations' but does not explicitly state when to use this tool versus alternatives like 'get_tmdb_details' (which might include credits) or 'person_details' (for individual credits). It provides some context but lacks explicit guidance on exclusions or comparisons with sibling tools.

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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