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mcp-metricool

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get_instagram_reels

Retrieve Instagram Reels data from your Metricool account by specifying a date range and blog ID. Extract and analyze Reels content for insights and performance tracking.

Instructions

Get the list of Instagram Reels from your Metricool 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 handler function for the 'get_instagram_reels' tool. It is registered via the @mcp.tool() decorator and fetches Instagram Reels data from the Metricool API using a GET request to the specified endpoint. Includes input schema in the function signature and docstring.
    @mcp.tool()
    async def get_instagram_reels(init_date: str, end_date: str, blog_id: int) -> str | dict[str, Any]:
        """
        Get the list of Instagram Reels from your Metricool 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/reels/instagram?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 Instagram Reels")
    
        return response
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 retrieving data but fails to describe key traits: whether this is a read-only operation, if it requires authentication, rate limits, pagination, error handling, or the format of returned data. For a data-fetching tool with zero annotation coverage, this is a significant gap.

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, with the main purpose stated first followed by parameter details. It avoids unnecessary fluff, but the parameter explanations could be slightly more integrated into the flow rather than listed as 'Args:'.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (3 parameters, no annotations, no output schema), the description is incomplete. It covers parameter semantics but lacks behavioral context (e.g., authentication needs, data format, error cases) and output details, which are critical for a data retrieval tool. This leaves significant gaps for the agent.

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?

Schema description coverage is 0%, so the description must compensate. It adds meaningful semantics by explaining each parameter's purpose and format (e.g., 'Init date of the period to get the data' with format 'YYYY-MM-DD'), which clarifies beyond the schema's basic types. However, it does not cover all potential nuances, such as date validation or blog ID sourcing, preventing a perfect score.

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 tool's purpose: 'Get the list of Instagram Reels from your Metricool account.' It specifies the verb ('Get'), resource ('Instagram Reels'), and source ('Metricool account'), which is clear and actionable. However, it does not explicitly differentiate from sibling tools like 'get_instagram_posts' or 'get_instagram_stories', which would require a 5.

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. It lacks context about prerequisites (e.g., authentication), exclusions, or comparisons to sibling tools such as 'get_instagram_posts' or 'get_facebook_reels'. This absence leaves the agent without usage direction.

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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