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drakonkat

wizzy-mcp-tmdb

by drakonkat
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

trending_tv

Discover popular TV shows trending today or this week for content analysis and audience insights.

Instructions

Retrieves trending TV shows. Input: time_window (required: day|week), page (optional), language (optional ISO 639-1). Output: JSON with paginated trending results. Purpose: Discover currently popular TV shows for trend analysis by AI agents.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
languageNo
pageNo
time_windowYes

Implementation Reference

  • mcp-tmdb-server.js:660-663 (handler)
    The asynchronous handler function that executes the 'trending_tv' tool logic: fetches trending TV shows from TMDB API using the provided parameters and returns the data as a JSON string in a content block.
    handler: async ({time_window, page, language}) => {
    const data = await tmdbFetch(`/trending/tv/${time_window}`, {page, language});
    return {content: [{type: 'text', text: JSON.stringify(data, null, 2)}]};
}
  • mcp-tmdb-server.js:650-659 (schema)
    The input schema defining the parameters for the 'trending_tv' tool: time_window (required, 'day' or 'week'), optional page and language.
    inputSchema: {
    type: "object",
    properties: {
        time_window: {type: "string", enum: ["day", "week"]},
        page: {type: "number", minimum: 1},
        language: {type: "string"}
    },
    required: ["time_window"],
    additionalProperties: false
},
  • mcp-tmdb-server.js:642-664 (registration)
    The complete tool definition object for 'trending_tv', including comments, name, description, input schema, and handler, which is included in the 'tools' array for MCP server registration.
    // Tool: trending_tv
// Purpose: Get trending TV shows.
// Input: time_window (required: day|week), page (optional), language (optional).
// Output: JSON with paginated trending TV results.
// Use case: AI agents can identify currently popular TV shows.
{
    name: "trending_tv",
    description: "Retrieves trending TV shows. Input: time_window (required: day|week), page (optional), language (optional ISO 639-1). Output: JSON with paginated trending results. Purpose: Discover currently popular TV shows for trend analysis by AI agents.",
    inputSchema: {
        type: "object",
        properties: {
            time_window: {type: "string", enum: ["day", "week"]},
            page: {type: "number", minimum: 1},
            language: {type: "string"}
        },
        required: ["time_window"],
        additionalProperties: false
    },
    handler: async ({time_window, page, language}) => {
        const data = await tmdbFetch(`/trending/tv/${time_window}`, {page, language});
        return {content: [{type: 'text', text: JSON.stringify(data, null, 2)}]};
    }
},

Tool Definition Quality

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: it retrieves data (implying read-only), specifies pagination ('paginated trending results'), and mentions the output format ('JSON'). However, it lacks details on rate limits, authentication needs, or error handling, which are common gaps for such tools.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core function, followed by input/output details and purpose, all in three concise sentences. Each sentence adds value without redundancy, making it efficient and easy to parse for an AI agent.

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 moderate complexity (3 parameters, no output schema, no annotations), the description is fairly complete: it covers purpose, parameters, output format, and usage intent. However, it could improve by addressing sibling tool differentiation more explicitly and providing more behavioral details like rate limits or error cases.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds meaningful context beyond the input schema: it explains that time_window is required with options 'day|week', page is optional for pagination, and language uses ISO 639-1. Since schema description coverage is 0%, this compensates well by clarifying parameter purposes and constraints, though it doesn't cover all nuances like page defaults.

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 trending TV shows') and resource ('TV shows'), distinguishing it from siblings like trending_movies and trending_people by specifying the media type. It also mentions the purpose ('Discover currently popular TV shows for trend analysis by AI agents'), which adds context beyond the basic function.

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 'trend analysis by AI agents' and 'currently popular TV shows,' suggesting it's for real-time or recent trends. However, it does not explicitly state when to use this tool versus alternatives like trending_all, trending_movies, or discover_tv, nor does it provide exclusions or prerequisites, leaving some ambiguity.

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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