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drakonkat

wizzy-mcp-tmdb

get_watch_providers

Find streaming services for movies and TV shows in specific regions. Input media type and region code to get available providers.

Instructions

Retrieves watch providers (streaming services) for movies or TV in a specific region. Input: type (required: movie|tv), language (optional ISO 639-1, default en), watch_region (required ISO 3166-1 code). Output: JSON with provider list. Purpose: Discover streaming availability for content recommendations by AI agents.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
languageNoISO 639-1 language (e.g., en)
typeYesMedia type for providers endpoint
watch_regionYesISO 3166-1 region code (e.g., IT)

Implementation Reference

  • The core handler function that implements the tool logic: fetches watch providers data from TMDB API using tmdbFetch with the specified type (movie/tv), language, and region, then returns it as a JSON-formatted text content block.
    handler: async ({type = "tv", language = "en", watch_region}) => {
        const data = await tmdbFetch(`/watch/providers/${type}`, {language, watch_region});
        return {content: [{type: "text", text: JSON.stringify(data, null, 2)}]};
    }
  • Input schema for validating tool parameters: requires 'type' ("movie" or "tv") and 'watch_region' (ISO 3166-1 code), optional 'language' (ISO 639-1).
    inputSchema: {
        type: "object",
        properties: {
            type: {type: "string", enum: ["movie", "tv"], description: "Media type for providers endpoint"},
            language: {type: "string", description: "ISO 639-1 language (e.g., en)"},
            watch_region: {type: "string", description: "ISO 3166-1 region code (e.g., IT)"}
        },
        required: ["watch_region", "type"],
        additionalProperties: false
    },
  • Full tool definition object registered in the 'tools' array, which is used by MCP server handlers for listing (ListToolsRequestSchema) and calling (CallToolRequestSchema) the tool.
    {
        name: "get_watch_providers",
        description: "Retrieves watch providers (streaming services) for movies or TV in a specific region. Input: type (required: movie|tv), language (optional ISO 639-1, default en), watch_region (required ISO 3166-1 code). Output: JSON with provider list. Purpose: Discover streaming availability for content recommendations by AI agents.",
        inputSchema: {
            type: "object",
            properties: {
                type: {type: "string", enum: ["movie", "tv"], description: "Media type for providers endpoint"},
                language: {type: "string", description: "ISO 639-1 language (e.g., en)"},
                watch_region: {type: "string", description: "ISO 3166-1 region code (e.g., IT)"}
            },
            required: ["watch_region", "type"],
            additionalProperties: false
        },
        handler: async ({type = "tv", language = "en", watch_region}) => {
            const data = await tmdbFetch(`/watch/providers/${type}`, {language, watch_region});
            return {content: [{type: "text", text: JSON.stringify(data, null, 2)}]};
        }
    },
Behavior2/5

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

With no annotations provided, the description carries full burden but only states it 'retrieves' data without disclosing behavioral traits like rate limits, authentication needs, data freshness, or error handling. It mentions the output format but doesn't describe pagination, response structure details, or potential limitations of the provider data.

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 with three sentences that efficiently cover purpose, inputs, outputs, and use case. It's front-loaded with the core functionality and avoids unnecessary elaboration, though the final sentence about AI agents could be integrated more smoothly.

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?

For a 3-parameter read-only tool with no output schema, the description covers basic functionality but lacks completeness regarding behavioral context. Without annotations or output schema, it should ideally describe more about the return structure, error conditions, or limitations to fully guide the 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 adds minimal value beyond the schema by mentioning 'specific region' context and the purpose of discovering streaming availability, but doesn't provide additional parameter semantics like format examples beyond what's in the schema descriptions.

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 specific verbs ('retrieves', 'discover') and resources ('watch providers for movies or TV'), and distinguishes it from siblings by focusing on streaming availability rather than discovery or search functions. It explicitly mentions the output format and AI agent use case.

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 content recommendations by AI agents') but doesn't explicitly state when to use this tool versus alternatives like discover_by_provider or search tools. It provides basic input requirements but lacks guidance on when this specific provider lookup is preferred over other discovery methods.

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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