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ilhankilic

YaparAI MCP Server

by ilhankilic

list_social_posts

List all published and scheduled social media posts for your organization, with optional filters for platform or specific account.

Instructions

List published and scheduled social media posts.

Returns all posts for the organization, with optional filtering by platform or specific account. Requires enterprise subscription.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
platformNoFilter by platform ("instagram", "facebook", "tiktok", "twitter")
account_idNoFilter by specific social account ID
org_idNoOrganization ID (uses YAPARAI_ORG_ID env var if not provided)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Main handler function for the list_social_posts MCP tool. Accepts optional platform, account_id, and org_id filters, then delegates to the HTTP client.
    async def list_social_posts(
        platform: Platform | None = None,
        account_id: str | None = None,
        org_id: str | None = None,
    ) -> dict:
        """
        List published and scheduled social media posts.
    
        Returns all posts for the organization, with optional filtering
        by platform or specific account. Requires enterprise subscription.
    
        Args:
            platform: Filter by platform ("instagram", "facebook", "tiktok", "twitter")
            account_id: Filter by specific social account ID
            org_id: Organization ID (uses YAPARAI_ORG_ID env var if not provided)
    
        Returns:
            List of posts with content, platform, published_at, and engagement stats.
        """
        oid = resolve_org_id(org_id)
        client = YaparAIClient()
        params: dict = {}
        if platform:
            params["platform"] = platform
        if account_id:
            params["account_id"] = account_id
        return await client.social_list_posts(oid, params or None)
  • Type definition for the Platform literal used as a parameter in list_social_posts.
    Platform = Literal["instagram", "facebook", "tiktok", "twitter"]
  • Registration of list_social_posts as an MCP tool on the server.
    mcp.tool(list_social_posts)
  • Import of list_social_posts in server.py where the tool is registered.
    # --- Enterprise: Social Media ---
    from yaparai.tools.social import (
        list_social_accounts,
        create_social_post,
        list_social_posts,
  • HTTP client method that sends a GET request to /api/enterprise/orgs/{org_id}/social/posts to fetch social posts.
    async def social_list_posts(self, org_id: str, params: dict | None = None) -> dict:
        """List social media posts."""
        return await self._request(
            "GET", f"/api/enterprise/orgs/{org_id}/social/posts", params=params
        )
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must disclose behavioral traits. It states the operation is read-only (listing) and mentions filtering, but lacks details on pagination, rate limits, ordering, or what 'all posts' means in terms of scope (e.g., time range, pagination).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, each adding value: first defines purpose, second adds scope and constraints. No redundant words. Information is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers purpose, filtering options, and subscription requirement. Since an output schema exists (not shown), return values are likely documented. Missing details like pagination or draft handling are minor for a list tool.

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 100%, so parameters are well-documented in the schema. The description repeats the filtering capability but adds value by mentioning the enterprise subscription prerequisite, which is not in the schema.

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 verb 'list' and resource 'published and scheduled social media posts'. It distinguishes from sibling tools like 'create_social_post' and 'get_social_quota' by focusing on listing existing posts.

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: it lists posts for the organization with optional filtering, and notes the enterprise subscription requirement. It does not explicitly state when not to use it or name alternatives, but the purpose is unambiguous.

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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