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by metricool

get_thread_posts

Retrieve thread posts from your Metricool brand account within a specified date range using a specific blog ID. Simplify data extraction for analysis or reporting.

Instructions

Get the list of Threads Posts 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_thread_posts' tool, registered via @mcp.tool(). It constructs the Metricool API URL for Threads posts and fetches the data using make_get_request, handling errors by returning a failure message.
    @mcp.tool()
    async def get_thread_posts(init_date: str, end_date: str, blog_id: int) -> str | dict[str, Any]:
        """
        Get the list of Threads Posts 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/threads?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 Threads Posts")
    
        return response
  • The @mcp.tool() decorator registers the get_thread_posts function as an MCP tool on the FastMCP instance.
    @mcp.tool()
  • Input schema defined by function parameters (init_date: str, end_date: str, blog_id: int) and detailed in docstring. Output is str (error) or dict[str, Any] (API response).
    async def get_thread_posts(init_date: str, end_date: str, blog_id: int) -> str | dict[str, Any]:
        """
        Get the list of Threads Posts 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.
        """
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 this is a read operation ('Get'), but doesn't describe important behaviors: whether it requires authentication, rate limits, pagination, error handling, or what the returned data structure looks like. For a data retrieval tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves.

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 first, followed by parameter details. The two-sentence structure is efficient with minimal waste. However, the parameter documentation could be slightly more concise by combining the date format explanation into one statement rather than repeating it for both date parameters.

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 3 parameters with 0% schema coverage, no annotations, and no output schema, the description is incomplete. It covers basic parameter formats but misses crucial context: authentication requirements, return format, error conditions, rate limits, and what constitutes a valid 'blog id.' For a data retrieval tool in a social media analytics context, users need to understand what data structure to expect and any access constraints.

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 schema provides no parameter documentation. The description compensates by documenting all 3 parameters with names and format requirements (YYYY-MM-DD for dates, integer for blog_id). However, it doesn't explain what a 'blog id' represents in the Metricool context, valid date ranges, or whether dates are inclusive/exclusive. The description adds meaningful value but doesn't fully compensate for the complete lack of schema documentation.

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 Threads Posts from your Metricool brand account.' It specifies the verb ('Get') and resource ('Threads Posts'), and distinguishes it from siblings by focusing on Threads platform posts rather than other social media platforms. However, it doesn't explicitly differentiate from similar list-retrieval tools like get_x_posts or get_instagram_posts beyond the platform name.

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 prerequisites (e.g., needing a Metricool account), compare it to similar tools like get_x_posts or get_instagram_posts, or indicate when this tool is preferred over others. The only implicit context is the platform-specific focus on Threads.

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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