google_ads_performance_report

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

With no annotations provided, the description carries the full responsibility for disclosing behavioral traits. It explicitly states 'Read-only; no mutation,' which is the most critical behavioral safety factor. It also describes the return shape in detail, including the exact structure of the metrics object. However, it does not mention potential edge cases like empty results, data freshness, or required permissions. Still, the explicit read-only declaration and detailed output shape provide strong transparency for a read-only aggregation tool.

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

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

The description is longer than some but every sentence contributes value: it states the action, the return shape (with metrics list), the read-only trait, and the alternatives. The information is front-loaded with the core purpose. It could be slightly tighter by omitting the metric list (since no output schema exists, that list is valuable), so it remains well-structured and not wasteful.

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

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

The tool has 3 parameters, no output schema, and no annotations. The description compensates by fully specifying the return structure (one row per campaign with a nested metrics object listing all fields). It also provides usage context and alternatives. The schema covers parameter details. The only missing piece is potential error/edge-case behavior, but for a read-only reporting tool this is not critical. Overall, the description is sufficiently complete for an agent to understand what the tool does and what it returns.

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

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

The schema description coverage is 100% and each parameter has a detailed description: 'period' explains the default and usage guidance, 'campaign_id' explains filtering vs. aggregation, and 'customer_id' explains the fallback behavior. The tool's description itself does not add further parameter semantics beyond what the schema already provides. Since the schema does the heavy lifting, the baseline score of 3 is appropriate.

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

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool's purpose: aggregate campaign-level performance metrics for a Google Ads account. It uses a specific verb ('Aggregate') and names the resource ('campaign-level performance metrics'). It explicitly distinguishes itself from sibling tools by naming google_ads_ad_performance_report, google_ads_network_performance_report, google_ads_search_terms_report, and google_ads_conversions_performance, making it unmistakable which tool to choose for campaign-level totals.

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

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

The description provides explicit when-to-use guidance: 'Use this for campaign-level totals.' It then lists clear alternatives for other use cases (per-ad, network splits, search terms, conversion actions). Additionally, the schema description for the 'period' parameter recommends shorter windows for recent changes and longer windows for trend baselines. This is exemplary differentiation and context.

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