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drakonkat

wizzy-mcp-tmdb

by drakonkat
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

tv_popular

Get popular TV series recommendations using paginated results. Filter by language and region to find shows trending in specific markets.

Instructions

Retrieves popular TV series. Input: page (optional), language (optional ISO 639-1), region (optional ISO 3166-1). Output: JSON with paginated results. Purpose: Access widely popular TV shows for general recommendations by AI agents.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
languageNo
pageNo
regionNo

Implementation Reference

  • mcp-tmdb-server.js:737-740 (handler)
    The handler function that executes the tv_popular tool. It fetches popular TV series data from the TMDB API endpoint '/tv/popular' using the tmdbFetch helper, with optional parameters for page, language, and region, and returns the JSON-stringified data wrapped in MCP content format.
    handler: async ({page, language, region}) => {
    const data = await tmdbFetch('/tv/popular', {page, language, region});
    return {content: [{type: 'text', text: JSON.stringify(data, null, 2)}]};
}
  • mcp-tmdb-server.js:732-736 (schema)
    The input schema defining the parameters for the tv_popular tool: optional 'page' (number >=1), 'language' (string, ISO 639-1), and 'region' (string, ISO 3166-1).
    inputSchema: {
    type: "object",
    properties: {page: {type: "number", minimum: 1}, language: {type: "string"}, region: {type: "string"}},
    additionalProperties: false
},
  • mcp-tmdb-server.js:729-741 (registration)
    The complete tool object definition for 'tv_popular' within the tools array, which is later used to register the tool's metadata for listing and its handler for execution in the MCP server request handlers.
    {
    name: "tv_popular",
    description: "Retrieves popular TV series. Input: page (optional), language (optional ISO 639-1), region (optional ISO 3166-1). Output: JSON with paginated results. Purpose: Access widely popular TV shows for general recommendations by AI agents.",
    inputSchema: {
        type: "object",
        properties: {page: {type: "number", minimum: 1}, language: {type: "string"}, region: {type: "string"}},
        additionalProperties: false
    },
    handler: async ({page, language, region}) => {
        const data = await tmdbFetch('/tv/popular', {page, language, region});
        return {content: [{type: 'text', text: JSON.stringify(data, null, 2)}]};
    }
},

Tool Definition Quality

C2.9/5.0
Behavior2/5

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 'paginated results' and 'popular TV series' but lacks critical details like rate limits, authentication requirements, data freshness, or what constitutes 'popular' (e.g., current popularity vs. all-time). For a read operation with zero annotation coverage, this leaves significant behavioral gaps.

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 three sentences: purpose, input parameters, and output format. Each sentence adds value without redundancy. It's appropriately sized for a simple retrieval tool, though the third sentence about 'general recommendations' could be considered slightly extraneous.

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 tool's low complexity (3 optional parameters, no output schema, no annotations), the description is moderately complete. It covers basic purpose, parameters, and output format but lacks behavioral details and sibling differentiation. For a read-only tool with simple inputs, it's adequate but has clear gaps in usage guidance and transparency.

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?

The description lists all three parameters (page, language, region) and provides some semantic context: language uses ISO 639-1, region uses ISO 3166-1, and page is for pagination. However, with 0% schema description coverage, it doesn't fully compensate by explaining parameter interactions, default values, or constraints beyond the basic schema types. The added value is moderate but incomplete.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Retrieves') and resource ('popular TV series'), making the purpose unambiguous. It distinguishes from siblings by specifying 'popular' shows rather than trending, airing today, or top-rated content. However, it doesn't explicitly contrast with 'discover_tv' or 'trending_tv', leaving some sibling differentiation incomplete.

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

Usage Guidelines2/5

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

The description provides minimal usage guidance, stating only that it's for 'general recommendations by AI agents.' It doesn't specify when to use this tool versus alternatives like 'trending_tv', 'tv_top_rated', or 'discover_tv', nor does it mention any prerequisites or exclusions. No explicit when/when-not instructions are provided.

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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