get_usage_summary

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

With no annotations provided, the description carries full burden but offers limited behavioral insight. It mentions the tool 'returns' specific data types (visits, counts, etc.) and breakdowns, indicating a read-only operation, but doesn't disclose critical traits like rate limits, authentication needs, data freshness, or error handling. For a tool with 6 parameters and no annotations, this is a significant gap in transparency.

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 efficiently structured in two sentences: the first states the core purpose and filters, the second details return values. It's front-loaded with key information and avoids redundancy. However, it could be slightly more concise by integrating the filter and return details more seamlessly, but overall it earns its place with zero waste.

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?

Given 6 parameters with full schema coverage but no annotations or output schema, the description is moderately complete. It covers the tool's purpose and return data, which helps compensate for the lack of output schema. However, for a read operation with multiple filters and grouping options, it should ideally mention output structure implications (e.g., how grouping affects the summary format) to be fully adequate.

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?

Schema description coverage is 100%, so the schema fully documents all 6 parameters with details like formats, defaults, and enums. The description adds minimal value beyond the schema by listing filter options (account, group, subscription) and mentioning grouping, but doesn't explain parameter interactions or semantics (e.g., how 'groupBy' affects output). Baseline 3 is appropriate as the schema does the heavy lifting.

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 states the tool's purpose: 'Get usage summary for a time period' with specific metrics like visits, unique counts, ratings, and call duration. It distinguishes itself from siblings like 'get_visit_trends' or 'get_rating_distribution' by offering a comprehensive summary rather than focused breakdowns. However, it doesn't explicitly contrast with all siblings (e.g., 'get_platform_breakdown'), keeping it from a perfect 5.

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 implies usage context by mentioning optional filters (account, group, subscription) and grouping, suggesting it's for aggregated analytics. However, it lacks explicit guidance on when to use this tool versus alternatives like 'get_visit_trends' for time-series data or 'get_subscription_breakdown' for detailed tier analysis. No exclusions or prerequisites are stated, leaving usage somewhat open-ended.

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