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drakonkat

wizzy-mcp-tmdb

search_tmdb_person

Search for actors, directors, and other film professionals in The Movie Database to support cast and crew analysis for AI agents.

Instructions

Searches for people (actors, directors, etc.) in TMDB. Input: query (required search string), page (optional), language (optional ISO 639-1), include_adult (optional boolean), region (optional ISO 3166-1). Output: JSON with paginated person results. Purpose: Discover individuals for cast/crew analysis by AI agents.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
include_adultNoInclude adult results
languageNoISO 639-1 code (e.g., en-US)
pageNoPage number
queryYesSearch query for people
regionNoISO 3166-1 region code (e.g., US)

Implementation Reference

  • The handler function that executes the search_tmdb_person tool logic by calling tmdbFetch on the TMDB /search/person endpoint and returning the JSON-stringified results.
    handler: async ({query, page, language, include_adult, region}) => {
        const data = await tmdbFetch('/search/person', {query, page, language, include_adult, region});
        return {content: [{type: 'text', text: JSON.stringify(data, null, 2)}]};
    }
  • The input schema for validating arguments to the search_tmdb_person tool.
    inputSchema: {
        type: "object",
        properties: {
            query: {type: "string", description: "Search query for people"},
            page: {type: "number", minimum: 1, description: "Page number"},
            language: {type: "string", description: "ISO 639-1 code (e.g., en-US)"},
            include_adult: {type: "boolean", description: "Include adult results"},
            region: {type: "string", description: "ISO 3166-1 region code (e.g., US)"},
        },
        required: ["query"],
        additionalProperties: false,
    },
  • The full tool registration object in the tools array, defining name, description, inputSchema, and handler for search_tmdb_person.
    {
        name: "search_tmdb_person",
        description: "Searches for people (actors, directors, etc.) in TMDB. Input: query (required search string), page (optional), language (optional ISO 639-1), include_adult (optional boolean), region (optional ISO 3166-1). Output: JSON with paginated person results. Purpose: Discover individuals for cast/crew analysis by AI agents.",
        inputSchema: {
            type: "object",
            properties: {
                query: {type: "string", description: "Search query for people"},
                page: {type: "number", minimum: 1, description: "Page number"},
                language: {type: "string", description: "ISO 639-1 code (e.g., en-US)"},
                include_adult: {type: "boolean", description: "Include adult results"},
                region: {type: "string", description: "ISO 3166-1 region code (e.g., US)"},
            },
            required: ["query"],
            additionalProperties: false,
        },
        handler: async ({query, page, language, include_adult, region}) => {
            const data = await tmdbFetch('/search/person', {query, page, language, include_adult, region});
            return {content: [{type: 'text', text: JSON.stringify(data, null, 2)}]};
        }
    },
  • The tmdbFetch helper function used by the handler to make authenticated API requests to the TMDB search/person endpoint.
    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 mentions the output format ('JSON with paginated person results'), which is helpful, but does not cover other behavioral aspects such as rate limits, authentication needs, error handling, or what 'include_adult' entails. The description adds some value but leaves gaps for a search tool.

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 and front-loaded with the core functionality. However, the parameter listing in the description is somewhat redundant given the schema coverage, and the purpose statement could be integrated more seamlessly. It's efficient but has minor structural inefficiencies.

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 complexity (a search tool with 5 parameters) and no annotations or output schema, the description is moderately complete. It covers purpose, input parameters, and output format, but lacks details on behavioral traits like pagination limits, error cases, or API constraints, which would be helpful for an AI agent.

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 all parameters thoroughly. The description lists parameters but does not add meaningful semantics beyond what the schema provides (e.g., it repeats parameter names without extra context). Baseline 3 is appropriate as the schema does the heavy lifting.

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 verb ('Searches for') and resource ('people (actors, directors, etc.) in TMDB'), and explicitly distinguishes this tool from siblings by specifying its focus on people rather than movies, TV shows, or other entities. The purpose statement at the end reinforces its role for 'cast/crew analysis by AI agents.'

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 implies usage context ('Discover individuals for cast/crew analysis') but does not explicitly state when to use this tool versus alternatives like 'person_details' or 'trending_people.' It provides some guidance through the purpose statement but lacks explicit exclusions or named alternatives.

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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