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wizzy-mcp-tmdb

search_tmdb_tv

Search for TV shows in The Movie Database using specific criteria like title, air date year, and language to find relevant series information.

Instructions

Searches specifically for TV shows in TMDB. Input: query (required search string), page (optional), language (optional ISO 639-1), first_air_date_year (optional year filter), include_adult (optional boolean). Output: JSON with paginated normalized results. Purpose: Targeted TV show discovery for AI-driven content queries.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
first_air_date_yearNoFilter by first air date year
include_adultNoInclude adult results
languageNoISO 639-1 code (e.g., en-US)
pageNoPage number
queryYesSearch query for TV shows

Implementation Reference

  • The main handler function for the search_tmdb_tv tool. It fetches TV show search results from TMDB API endpoint '/search/tv', normalizes the results using mapSearchResult, and returns a formatted JSON response with pagination info.
    handler: async ({query, page, language, first_air_date_year, include_adult}) => {
        const data = await tmdbFetch('/search/tv', {query, page, language, first_air_date_year, include_adult});
        const results = (data.results || []).map(mapSearchResult);
        return {
            content: [{
                type: 'text',
                text: JSON.stringify({
                    page: data.page,
                    total_pages: data.total_pages,
                    total_results: data.total_results,
                    results
                }, null, 2)
            }]
        };
    }
  • Input schema for validating parameters to the search_tmdb_tv tool, including required 'query' and optional filters.
    inputSchema: {
        type: "object",
        properties: {
            query: {type: "string", description: "Search query for TV shows"},
            page: {type: "number", minimum: 1, description: "Page number"},
            language: {type: "string", description: "ISO 639-1 code (e.g., en-US)"},
            first_air_date_year: {type: "number", description: "Filter by first air date year"},
            include_adult: {type: "boolean", description: "Include adult results"},
        },
        required: ["query"],
        additionalProperties: false,
    },
  • The tool registration object in the 'tools' array, defining name, description, inputSchema, and handler for search_tmdb_tv.
    {
        name: "search_tmdb_tv",
        description: "Searches specifically for TV shows in TMDB. Input: query (required search string), page (optional), language (optional ISO 639-1), first_air_date_year (optional year filter), include_adult (optional boolean). Output: JSON with paginated normalized results. Purpose: Targeted TV show discovery for AI-driven content queries.",
        inputSchema: {
            type: "object",
            properties: {
                query: {type: "string", description: "Search query for TV shows"},
                page: {type: "number", minimum: 1, description: "Page number"},
                language: {type: "string", description: "ISO 639-1 code (e.g., en-US)"},
                first_air_date_year: {type: "number", description: "Filter by first air date year"},
                include_adult: {type: "boolean", description: "Include adult results"},
            },
            required: ["query"],
            additionalProperties: false,
        },
        handler: async ({query, page, language, first_air_date_year, include_adult}) => {
            const data = await tmdbFetch('/search/tv', {query, page, language, first_air_date_year, include_adult});
            const results = (data.results || []).map(mapSearchResult);
            return {
                content: [{
                    type: 'text',
                    text: JSON.stringify({
                        page: data.page,
                        total_pages: data.total_pages,
                        total_results: data.total_results,
                        results
                    }, null, 2)
                }]
            };
        }
    },
  • Helper function to normalize TMDB search results into a compact format (id, media_type, title, etc.) used in the tool handler.
    function mapSearchResult(item) {
        const media_type = item.media_type || (item.title ? "movie" : item.name ? "tv" : "unknown");
        const title = item.title || item.name || "";
        const date = item.release_date || item.first_air_date || "";
        return {
            id: item.id,
            media_type,
            title,
            date,
            original_language: item.original_language,
            popularity: item.popularity,
            vote_average: item.vote_average,
            overview: item.overview,
        };
    }
  • Core helper function that performs HTTP requests to the TMDB API, handles authentication with API key, and parses JSON responses. Used by the tool handler.
    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?

No annotations are provided, so the description carries the full burden. It discloses that output is 'JSON with paginated normalized results', which adds useful behavioral context beyond the input schema. However, it lacks details on rate limits, authentication needs, or error handling that would be helpful 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 purpose. However, the second sentence listing all parameters is somewhat redundant with the schema and could be more concise, though it does not severely impact readability.

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 and no output schema, the description provides basic purpose and output format, but lacks completeness for a search tool with 5 parameters. It does not cover error cases, rate limits, or example usage, which would enhance contextual understanding 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 significant meaning beyond what the schema provides, such as explaining how 'first_air_date_year' interacts with 'query' or default behaviors. Baseline 3 is appropriate given high schema 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 a specific verb ('searches') and resource ('TV shows in TMDB'), distinguishing it from siblings like search_tmdb_movies and search_tmdb_person. It explicitly mentions 'targeted TV show discovery' which reinforces the specialization.

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 'AI-driven content queries' and TV show discovery, but does not explicitly state when to use this tool versus alternatives like discover_tv or trending_tv. No exclusions or specific contexts are provided beyond the general purpose.

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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