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by metricool

get_scheduled_posts

Retrieve scheduled posts for a Metricool brand account within a specified date range and timezone. Extend search range if needed to access unpublished posts efficiently.

Instructions

Get the list of scheduled posts for a specific Metricool brand (blog_id). Only retrieves posts that are scheduled (not yet published). If the user doesn't provide a blog_id, ask for it.

Args: blog_id: Blog id of the Metricool brand account. start: Start date of the period to get the data. The format is YYYY-MM-DD end: End date of the period to get the data. The format is YYYY-MM-DD timezone: Timezone of the post. The format is "Europe%2FMadrid". Use the timezone of the user extracted from the get_brands tool. extendedRange: When it's true, search date range is expanded one day after and one day before. Default value is false.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
blog_idYes
endYes
extendedRangeYes
startYes
timezoneYes

Implementation Reference

  • The handler function decorated with @mcp.tool() that implements the logic for retrieving scheduled posts from the Metricool API using a GET request to the scheduler/posts endpoint.
    @mcp.tool()
    async def get_scheduled_posts(blog_id: int, start: str, end: str, timezone: str, extendedRange: bool) -> str | dict[str, Any]:
        """
        Get the list of scheduled posts for a specific Metricool brand (blog_id).
        Only retrieves posts that are scheduled (not yet published).
        If the user doesn't provide a blog_id, ask for it.
    
        Args:
         blog_id: Blog id of the Metricool brand account.
         start: Start date of the period to get the data. The format is YYYY-MM-DD
         end: End date of the period to get the data. The format is YYYY-MM-DD
         timezone: Timezone of the post. The format is "Europe%2FMadrid".  Use the timezone of the user extracted from the get_brands tool.
         extendedRange: When it's true, search date range is expanded one day after and one day before. Default value is false.
        """
        url = f"{METRICOOL_BASE_URL}/v2/scheduler/posts?blogId={blog_id}&userId={METRICOOL_USER_ID}&integrationSource=MCP&start={start}T00%3A00%3A00&end={end}T23%3A59%3A59&timezone={timezone}&extendedRange={extendedRange}"
    
        response = await make_get_request(url)
    
        if not response:
            return "Failed to get scheduled posts"
    
        return response
  • Initialization of the FastMCP server instance 'metricool'. All tools, including get_scheduled_posts, are registered to this instance via @mcp.tool() decorators.
    mcp = FastMCP("metricool")
  • Imports the tools module which triggers the registration of all @mcp.tool() functions, including get_scheduled_posts, upon import.
    from .tools import tools
  • Docstring providing detailed input parameter descriptions and usage instructions, serving as the tool schema.
    """
    Get the list of scheduled posts for a specific Metricool brand (blog_id).
    Only retrieves posts that are scheduled (not yet published).
    If the user doesn't provide a blog_id, ask for it.
    
    Args:
     blog_id: Blog id of the Metricool brand account.
     start: Start date of the period to get the data. The format is YYYY-MM-DD
     end: End date of the period to get the data. The format is YYYY-MM-DD
     timezone: Timezone of the post. The format is "Europe%2FMadrid".  Use the timezone of the user extracted from the get_brands tool.
     extendedRange: When it's true, search date range is expanded one day after and one day before. Default value is false.
    """
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. It mentions that it 'Only retrieves posts that are scheduled (not yet published)' and includes a note about asking for blog_id if missing, which adds some behavioral context. However, it lacks details on permissions, rate limits, pagination, or error handling, which are important for a tool with 5 required parameters and no output schema. The description does not contradict any annotations, but it is insufficient for full transparency.

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 well-structured and appropriately sized. It starts with a clear purpose statement, followed by a bullet-point list of parameters with concise explanations. Every sentence adds value, with no redundant information. It could be slightly more front-loaded by integrating parameter details into the initial description, but overall it is efficient and easy to parse.

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 the complexity (5 required parameters, no annotations, no output schema), the description is partially complete. It excels in parameter semantics and purpose clarity but falls short in behavioral transparency (e.g., missing details on response format, error cases, or performance). Without an output schema, the description should ideally hint at return values, but it does not. It is adequate for basic use but has clear gaps for robust agent operation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must fully compensate. It provides detailed semantics for all 5 parameters: blog_id (Blog id of the Metricool brand account), start and end (date formats YYYY-MM-DD), timezone (format 'Europe%2FMadrid', sourced from get_brands), and extendedRange (expands search range, default false). This adds significant meaning beyond the bare schema, fully documenting each parameter's purpose and format.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

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 scheduled posts for a specific Metricool brand (blog_id). Only retrieves posts that are scheduled (not yet published).' It specifies the exact resource (scheduled posts), scope (for a specific brand, not yet published), and distinguishes it from siblings like 'get_analytics' or 'post_schedule_post' by focusing on retrieval of scheduled content only.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for usage: 'If the user doesn't provide a blog_id, ask for it.' It implies when to use this tool (to get scheduled posts) but does not explicitly state when not to use it or name alternatives like 'get_analytics' for different data. The guidance is helpful but lacks explicit exclusions or sibling comparisons.

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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