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get_catalog_info

Get product catalog details including product count, vertical, name, and connected pixel event sources by providing a catalog ID.

Instructions

Get catalog details including product count, vertical, name, and connected event sources (pixels).

Args: catalog_id: Product catalog ID (numeric string).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
catalog_idYes

Implementation Reference

  • The main handler function for the get_catalog_info tool. Calls Meta's Graph API to fetch catalog details (id, name, product_count, vertical, da_display_settings) and enriches with connected pixels, product sets, feeds, and rate limit info.
    def get_catalog_info(catalog_id: str) -> dict:
        """
        Get catalog details including product count, vertical, name,
        and connected event sources (pixels).
    
        Args:
            catalog_id: Product catalog ID (numeric string).
        """
        api_client._ensure_initialized()
    
        try:
            result = api_client.graph_get(
                f"/{catalog_id}",
                fields=[
                    "id", "name", "product_count", "vertical",
                    "da_display_settings",
                ],
            )
    
            # Get connected pixels (external_event_sources)
            try:
                event_sources = api_client.graph_get(
                    f"/{catalog_id}/external_event_sources",
                    fields=["id", "name"],
                )
                result["connected_pixels"] = event_sources.get("data", [])
            except MetaAPIError:
                result["connected_pixels"] = []
    
            # Get product sets
            try:
                sets_result = api_client.graph_get(
                    f"/{catalog_id}/product_sets",
                    fields=["id", "name", "product_count"],
                )
                result["product_sets"] = sets_result.get("data", [])
            except MetaAPIError:
                result["product_sets"] = []
    
            # Get feeds
            try:
                feeds_result = api_client.graph_get(
                    f"/{catalog_id}/product_feeds",
                    fields=["id", "name", "product_count", "latest_upload", "schedule"],
                )
                result["feeds"] = feeds_result.get("data", [])
            except MetaAPIError:
                result["feeds"] = []
    
            result["rate_limit_usage_pct"] = api_client.rate_limits.max_usage_pct
            return result
    
        except MetaAPIError:
            raise
  • The @mcp.tool() decorator registers this function as an MCP tool named 'get_catalog_info'.
    @mcp.tool()
    def get_catalog_info(catalog_id: str) -> dict:
  • The type signature (catalog_id: str -> dict) and docstring define the input schema for the tool.
    def get_catalog_info(catalog_id: str) -> dict:
        """
        Get catalog details including product count, vertical, name,
        and connected event sources (pixels).
    
        Args:
            catalog_id: Product catalog ID (numeric string).
        """
Behavior2/5

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

No annotations provided, so description carries full burden. It does not disclose side effects, read-only nature (though implied by 'get'), auth requirements, rate limits, or error behavior. Lacks transparency for a tool with no annotations.

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?

Description is short and front-loaded with a clear summary followed by parameter docs. Every sentence adds value, though the structure could be more standardized.

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 no output schema, the description should explain return format or structure; it only lists fields without detail. Also lacks error scenarios or prerequisites. Incomplete for a simple retrieval 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 coverage is 0%, so description must compensate. It adds context that catalog_id is a 'numeric string', which is helpful but minimal. Does not specify constraints like length or 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?

Description clearly specifies the verb 'get' and resource 'catalog details', listing specific fields (product count, vertical, name, pixels). Distinguishes itself from siblings like get_catalog_products by focusing on catalog-level metadata.

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?

No explicit guidance on when to use or avoid this tool, nor does it compare with alternatives among the many sibling tools. Usage is only implied by the purpose.

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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