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get_youtube_videos

Retrieve YouTube videos from your Metricool brand account for a specified date range and blog ID, streamlining video data access and analysis.

Instructions

Get the list of Youtube Videos from your Metricool brand account.

Args: init date: Init date of the period to get the data. The format is YYYY-MM-DD end date: End date of the period to get the data. The format is YYYY-MM-DD blog id: Blog id of the Metricool brand account.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
blog_idYes
end_dateYes
init_dateYes

Implementation Reference

  • The core handler implementation for the 'get_youtube_videos' tool. It is registered via the @mcp.tool() decorator and fetches YouTube video analytics data from the Metricool API for a given date range and blog ID. Includes type hints and docstring serving as input schema.
    @mcp.tool()
    async def get_youtube_videos(init_date: str, end_date: str, blog_id: int) -> str | dict[str, Any]:
        """
        Get the list of Youtube Videos from your Metricool brand account.
    
        Args:
         init date: Init date of the period to get the data. The format is YYYY-MM-DD
         end date: End date of the period to get the data. The format is YYYY-MM-DD
         blog id: Blog id of the Metricool brand account.
        """
    
        url = f"{METRICOOL_BASE_URL}/v2/analytics/posts/youtube?from={init_date}T00%3A00%3A00&to={end_date}T23%3A59%3A59&blogId={blog_id}&userId={METRICOOL_USER_ID}&integrationSource=MCP"
    
        response = await make_get_request(url)
    
        if not response:
            return ("Failed to get Youtube Videos")
    
        return response
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 fetching data but lacks details on permissions required, rate limits, pagination, error handling, or the format of returned data (e.g., list structure, fields included). This leaves significant gaps for an agent to understand how the tool behaves beyond basic input.

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 main purpose followed by parameter details in a clear 'Args:' section. There is no redundant or verbose language, making it efficient, though it could be slightly more structured (e.g., bullet points) for enhanced 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 the tool's moderate complexity (3 parameters, no output schema, no annotations), the description is partially complete: it covers parameter semantics well but lacks behavioral details like response format, error cases, or usage context. This makes it adequate for basic use but insufficient for full agent understanding without additional inference.

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 for all three parameters: it explains that 'init date' and 'end date' define a period for data retrieval with specific format (YYYY-MM-DD), and 'blog id' relates to a Metricool brand account. Since schema description coverage is 0%, this compensates well by clarifying parameter purposes beyond the schema's basic types and titles.

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 action ('Get the list of Youtube Videos') and specifies the resource ('from your Metricool brand account'), making the purpose evident. However, it doesn't explicitly differentiate from sibling tools like 'get_tiktok_videos' or 'get_twitch_videos' beyond mentioning 'Youtube Videos', which slightly limits differentiation.

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 no guidance on when to use this tool versus alternatives, such as other 'get_' tools for different platforms (e.g., 'get_tiktok_videos'), nor does it mention prerequisites like authentication or account setup. Usage is implied by the tool name and parameters but not explicitly stated.

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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