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drakonkat

wizzy-mcp-tmdb

person_details

Retrieve detailed information about actors, directors, and other film professionals from The Movie Database, including biographies, credits, and additional data for content analysis.

Instructions

Retrieves detailed information about a person (actor, director, etc.) from TMDB. Input: person_id (required TMDB ID), language (optional ISO 639-1 code), append (optional comma-separated fields like images,combined_credits,external_ids). Output: JSON with biography, birth/death info, and appended data. Purpose: Get comprehensive person profiles for AI-driven content analysis or recommendations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
appendNoComma-separated append_to_response (e.g., images,combined_credits,external_ids)
languageNoISO 639-1 code (e.g., en-US)
person_idYesTMDB Person ID

Implementation Reference

  • The asynchronous handler function for the 'person_details' tool. It fetches detailed person information from the TMDB API using the tmdbFetch helper and returns the data as a formatted JSON text content block.
    handler: async ({person_id, language, append}) => {
        const data = await tmdbFetch(`/person/${person_id}`, {language, append_to_response: append});
        return {content: [{type: "text", text: JSON.stringify(data, null, 2)}]};
    }
  • The input schema defining the parameters for the 'person_details' tool: person_id (required number), language (optional string), append (optional string for additional fields).
    inputSchema: {
        type: "object",
        properties: {
            person_id: {type: "number", description: "TMDB Person ID"},
            language: {type: "string", description: "ISO 639-1 code (e.g., en-US)"},
            append: {
                type: "string",
                description: "Comma-separated append_to_response (e.g., images,combined_credits,external_ids)"
            }
        },
        required: ["person_id"],
        additionalProperties: false
    },
  • The complete tool registration object for 'person_details' added to the tools array, which is used by the MCP server's listTools and callTool request handlers.
    {
        name: "person_details",
        description: "Retrieves detailed information about a person (actor, director, etc.) from TMDB. Input: person_id (required TMDB ID), language (optional ISO 639-1 code), append (optional comma-separated fields like images,combined_credits,external_ids). Output: JSON with biography, birth/death info, and appended data. Purpose: Get comprehensive person profiles for AI-driven content analysis or recommendations.",
        inputSchema: {
            type: "object",
            properties: {
                person_id: {type: "number", description: "TMDB Person ID"},
                language: {type: "string", description: "ISO 639-1 code (e.g., en-US)"},
                append: {
                    type: "string",
                    description: "Comma-separated append_to_response (e.g., images,combined_credits,external_ids)"
                }
            },
            required: ["person_id"],
            additionalProperties: false
        },
        handler: async ({person_id, language, append}) => {
            const data = await tmdbFetch(`/person/${person_id}`, {language, append_to_response: append});
            return {content: [{type: "text", text: JSON.stringify(data, null, 2)}]};
        }
    },
  • Helper function tmdbFetch used by the person_details handler (and other tools) to make authenticated API requests to the TMDB proxy 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 full burden. It discloses the read-only nature ('Retrieves'), output format ('JSON with biography, birth/death info, and appended data'), and data source ('TMDB'), but doesn't mention rate limits, authentication requirements, error conditions, or pagination behavior. For a tool with no annotation coverage, this provides basic behavioral context but lacks important operational details.

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 efficiently structured in two sentences: the first covers the core functionality and parameters, the second states the output and purpose. Every sentence adds value, though the parameter listing in parentheses is somewhat redundant with the schema. It's appropriately sized and front-loaded with the main action.

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 3 parameters with 100% schema coverage but no annotations and no output schema, the description provides adequate but incomplete context. It covers the basic purpose and output format but lacks details about error handling, rate limits, authentication, and specific behavioral constraints. For a read-only retrieval tool, this is minimally viable but could be more 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 description coverage is 100%, so the schema already fully documents all three parameters. The description adds minimal value beyond the schema - it mentions the parameters in parentheses but doesn't provide additional semantic context about how they affect the retrieval or what the 'append' fields actually contain. Baseline 3 is appropriate when 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 specific action ('Retrieves detailed information'), target resource ('about a person from TMDB'), and scope ('comprehensive person profiles'). It distinguishes this tool from siblings like search_tmdb_person (which likely searches rather than retrieves details) and movie_credits/tv_credits (which focus on credits rather than full profiles).

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 context ('for AI-driven content analysis or recommendations') but doesn't explicitly state when to use this tool versus alternatives. It doesn't mention when not to use it or directly compare to sibling tools like get_tmdb_details (which might handle other entity types) or search_tmdb_person (which might be for finding people rather than getting their full details).

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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