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acamolese

Google Search Console Audit MCP

gsc_performance_overview

Retrieve total clicks, impressions, average CTR, and average position for a specified site and date range.

Instructions

Summary of site performance (total clicks, impressions, avg CTR, avg position).

Args: site_url: Site URL (e.g. "https://example.com/" or "sc-domain:example.com"). date_from: Start date (YYYY-MM-DD). date_to: End date (YYYY-MM-DD).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
site_urlYes
date_fromYes
date_toYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The handler function for the 'gsc_performance_overview' tool. It calls the Search Analytics API (no dimensions) to fetch aggregate metrics (clicks, impressions, CTR, avg position) for the given site and date range, and returns them as a JSON string.
    @mcp.tool()
    def gsc_performance_overview(site_url: str, date_from: str, date_to: str) -> str:
        """Summary of site performance (total clicks, impressions, avg CTR, avg position).
    
        Args:
            site_url: Site URL (e.g. "https://example.com/" or "sc-domain:example.com").
            date_from: Start date (YYYY-MM-DD).
            date_to: End date (YYYY-MM-DD).
        """
        encoded = urllib.parse.quote(site_url, safe="")
        data = _api_post(
            f"{BASE}/sites/{encoded}/searchAnalytics/query",
            {"startDate": date_from, "endDate": date_to},
        )
        rows = data.get("rows", [])
        if not rows:
            return json.dumps({"clicks": 0, "impressions": 0, "ctr": "0.0000", "position": "0.0"})
        row = rows[0]
        return json.dumps(
            {
                "clicks": row.get("clicks", 0),
                "impressions": row.get("impressions", 0),
                "ctr": f"{row.get('ctr', 0):.4f}",
                "position": f"{row.get('position', 0):.1f}",
            },
            indent=2,
        )
  • Docstring/type definitions for the tool's inputs (site_url: str, date_from: str, date_to: str).
    """Summary of site performance (total clicks, impressions, avg CTR, avg position).
    
    Args:
        site_url: Site URL (e.g. "https://example.com/" or "sc-domain:example.com").
        date_from: Start date (YYYY-MM-DD).
        date_to: End date (YYYY-MM-DD).
    """
  • The @mcp.tool() decorator that registers the function as an MCP tool with the FastMCP server.
    @mcp.tool()
Behavior2/5

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

No annotations provided. Description lacks behavioral details such as data aggregation, time zone handling, quota limits, or whether it returns cached or real-time data.

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?

Short, front-loaded with purpose, and uses clear parameter documentation. No superfluous information.

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?

Output schema exists, reducing need to describe returns. However, no annotations and lack of usage context make it incomplete for a complex tool; leaves agent uninformed about behavioral nuances.

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%, but description provides format examples for site_url (https:// or sc-domain:) and date format (YYYY-MM-DD). This adds basic meaning, though could be more explicit about allowed patterns.

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 provides a 'Summary of site performance' with specific metrics (clicks, impressions, avg CTR, avg position). The verb 'overview' and metric list distinguish it from more detailed sibling tools like gsc_query, though not explicitly differentiated.

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 guidance on when to use this overview versus other tools. Does not mention prerequisites, use cases, or alternatives among siblings such as gsc_query for detailed queries.

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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