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get_facebook_stories

Retrieve Facebook Stories data from your Metricool brand account by specifying the blog ID and date range. Extract story insights for analysis or reporting purposes.

Instructions

Get the list of Facebook Stories 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 main handler function for the 'get_facebook_stories' tool. It is decorated with @mcp.tool(), which registers it as an MCP tool. The function fetches Facebook stories data from Metricool API using a GET request, with input parameters for date range and blog ID. The docstring provides the schema description.
    @mcp.tool()
    async def get_facebook_stories(init_date: str, end_date: str, blog_id: int) -> str | dict[str, Any]:
        """
        Get the list of Facebook Stories 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/stories/facebook?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 Facebook Stories")
    
        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 states the tool retrieves data ('Get'), implying a read-only operation, but doesn't clarify aspects like authentication requirements, rate limits, pagination, error handling, or the format/structure of the returned list. For a data-fetching tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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 core purpose stated first followed by parameter details. The sentences are efficient and avoid redundancy. It could be slightly more structured by separating usage notes, but overall, it's concise and well-organized.

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 basic parameter semantics but lacks behavioral details (e.g., authentication, response format), usage guidelines, and output information. For a tool that fetches data with multiple inputs, this leaves too many contextual gaps for effective agent use.

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 0%, so the description must compensate. It adds meaning by explaining that 'init_date' and 'end_date' define a period for data retrieval and specifying their format (YYYY-MM-DD), and it clarifies that 'blog_id' refers to a Metricool brand account. However, it doesn't detail what a 'blog_id' is, how to obtain it, or any constraints on date ranges, leaving some parameter semantics unclear.

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 Facebook Stories from your Metricool brand account.' It specifies the verb ('Get'), resource ('Facebook Stories'), and scope ('from your Metricool brand account'), making it easy to understand. However, it doesn't explicitly differentiate from sibling tools like 'get_facebook_posts' or 'get_instagram_stories', which is why it doesn't earn 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 doesn't mention sibling tools like 'get_facebook_posts' or 'get_instagram_stories' for comparison, nor does it specify prerequisites or exclusions. The only implied context is the need for a Metricool brand account, but this is insufficient for effective tool selection.

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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