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wizzy-mcp-tmdb

by drakonkat
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

tv_top_rated

Retrieve top-rated TV series to find quality content recommendations. Filter results by page, language, or region for targeted TV show discovery.

Instructions

Retrieves top-rated TV series. Input: page (optional), language (optional ISO 639-1), region (optional ISO 3166-1). Output: JSON with paginated results. Purpose: Access highly rated TV shows for quality content recommendations by AI agents.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
languageNo
pageNo
regionNo

Implementation Reference

  • mcp-tmdb-server.js:701-704 (handler)
    The handler function for the 'tv_top_rated' tool. It fetches top-rated TV series data from the TMDB API endpoint '/tv/top_rated' using the shared tmdbFetch utility, with optional parameters for page, language, and region, and returns the JSON-stringified response wrapped in the MCP content format.
    handler: async ({page, language, region}) => {
    const data = await tmdbFetch('/tv/top_rated', {page, language, region});
    return {content: [{type: 'text', text: JSON.stringify(data, null, 2)}]};
}
  • mcp-tmdb-server.js:696-700 (schema)
    The input schema for the 'tv_top_rated' tool, specifying an object with optional properties: page (number, minimum 1), language (string), and region (string). No additional properties allowed.
    inputSchema: {
    type: "object",
    properties: {page: {type: "number", minimum: 1}, language: {type: "string"}, region: {type: "string"}},
    additionalProperties: false
},
  • mcp-tmdb-server.js:693-705 (registration)
    The registration object for the 'tv_top_rated' tool within the tools array. This object defines the tool's name, description, input schema, and handler function, which is used by the MCP server for listing and calling the tool.
    {
    name: "tv_top_rated",
    description: "Retrieves top-rated TV series. Input: page (optional), language (optional ISO 639-1), region (optional ISO 3166-1). Output: JSON with paginated results. Purpose: Access highly rated TV shows for quality content 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/top_rated', {page, language, region});
        return {content: [{type: 'text', text: JSON.stringify(data, null, 2)}]};
    }
},

Tool Definition Quality

A3.6/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'paginated results' and 'JSON output', which adds some context, but fails to disclose critical details such as rate limits, authentication requirements, error handling, or what specific data fields are included in the results. For a read operation with no annotation coverage, this leaves significant 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 appropriately sized and front-loaded, starting with the core purpose. The second sentence efficiently covers parameters and output, and the third sentence reinforces usage context. While concise, the final sentence could be integrated more seamlessly, but overall, there is minimal waste.

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 moderate complexity (3 optional parameters, no output schema, no annotations), the description is adequate but incomplete. It covers purpose, parameters, and output format, but lacks details on behavioral aspects like rate limits or error handling. Without annotations or output schema, more context on result structure and operational constraints would be beneficial.

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 semantics: 'page (optional)', 'language (optional ISO 639-1)', 'region (optional ISO 3166-1)'. This adds meaning beyond the input schema, which has 0% description coverage. However, it does not explain how these parameters affect the results (e.g., region filtering content availability), leaving room for improvement.

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 ('Retrieves') and resource ('top-rated TV series'), making the purpose specific and unambiguous. It distinguishes this tool from siblings like 'trending_tv' or 'tv_popular' by focusing on 'top-rated' content, which implies a quality-based ranking rather than popularity or recency.

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 provides clear context for usage ('Access highly rated TV shows for quality content recommendations by AI agents'), indicating this tool is for recommendation scenarios. However, it does not explicitly state when to use alternatives like 'tv_popular' or 'trending_tv', nor does it mention any exclusions or prerequisites for use.

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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