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mcp-metricool

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by metricool

get_pinterest_pins

Retrieve Pinterest Pins from your Metricool brand account by specifying a date range and blog ID to analyze and manage your Pinterest content effectively.

Instructions

Get the list of Pinterest Pins 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 function for the 'get_pinterest_pins' tool. It is decorated with @mcp.tool() which registers it with the FastMCP server. The function fetches Pinterest pins data from the Metricool API using a GET request based on the provided date range and blog_id.
    @mcp.tool()
    async def get_pinterest_pins(init_date: str, end_date: str, blog_id: int) -> str | dict[str, Any]:
        """
        Get the list of Pinterest Pins 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/pinterest?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 Pinterest Pins")
    
        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 full burden. It states it 'gets' data, implying a read-only operation, but doesn't disclose behavioral traits like rate limits, pagination, error handling, or authentication requirements. For a data-fetching tool with zero annotation coverage, this leaves significant gaps in understanding how it 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 in the first sentence. The parameter explanations are clear and direct, with no wasted words. However, the formatting with 'Args:' and bullet-like lines is slightly informal but still effective.

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 3 parameters, no annotations, and no output schema, the description is minimally adequate. It covers the purpose and parameters but lacks behavioral context, usage guidelines, and output details. For a data retrieval tool, this leaves the agent with incomplete information about what to expect in return or operational constraints.

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?

With 0% schema description coverage, the description compensates well by explaining all three parameters: init_date (format YYYY-MM-DD), end_date (format YYYY-MM-DD), and blog_id (blog id of the Metricool brand account). This adds crucial meaning beyond the bare schema, though it doesn't detail constraints like date ranges or blog_id sourcing.

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') and resource ('Pinterest Pins from your Metricool brand account'), which is specific and unambiguous. It distinguishes from siblings like get_pinterest_boards by focusing on pins rather than boards. However, it doesn't explicitly contrast with other data-fetching tools like get_analytics or get_metrics, 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. It doesn't mention prerequisites (e.g., authentication needs), exclusions, or comparisons to siblings like get_pinterest_boards or other social media data tools. The agent must infer usage solely from the tool name and parameters.

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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