Skip to main content
Glama
metricool

mcp-metricool

Official
by metricool

get_instagram_posts

Retrieve Instagram post data from your Metricool account by specifying a date range and blog ID. Extract post details for analysis or reporting.

Instructions

Get the list of Instagram Posts 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 decorated with @mcp.tool(), which registers and implements the get_instagram_posts tool. It makes a GET request to the Metricool API to retrieve Instagram posts within a specified date range for a given blog ID.
    @mcp.tool()
    async def get_instagram_posts(init_date: str, end_date: str, blog_id: int) -> str | dict[str, Any]:
        """
        Get the list of Instagram Posts 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/posts/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 Posts")
    
        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 the tool retrieves data ('Get the list'), implying a read-only operation, but doesn't specify authentication requirements, rate limits, pagination, error handling, or what the returned data structure looks like. 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: the first sentence states the core purpose, followed by a structured 'Args:' section for parameters. There's no unnecessary fluff, and each sentence serves a clear purpose. It could be slightly more concise by integrating the parameter details into the main description, but overall it's efficient.

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 tool's complexity (3 parameters, no annotations, no output schema), the description is incomplete. It covers the basic purpose and parameters but lacks critical context: no information on authentication, rate limits, pagination, error handling, or the structure of returned data. For a data retrieval tool in a suite with many siblings, this leaves the agent with insufficient guidance to use it effectively.

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 provides clear semantics for all three parameters: 'init date' and 'end date' define the period with format 'YYYY-MM-DD', and 'blog id' identifies the Metricool brand account. This adds meaningful context beyond the schema's basic titles and types, though it doesn't explain where to find the 'blog id' or validate date ranges.

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 Posts from your Metricool account.' This specifies the verb ('Get'), resource ('Instagram Posts'), and source ('Metricool account'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'get_instagram_reels' or 'get_instagram_stories' beyond mentioning 'Posts' in the 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 sibling tools like 'get_instagram_reels' or 'get_instagram_stories' for different content types, or 'get_analytics' for performance data. The only context is the implied date range filtering, but no explicit usage scenarios or exclusions are stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Related Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/metricool/mcp-metricool'

If you have feedback or need assistance with the MCP directory API, please join our Discord server