aggregate-logs

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

No annotations provided, and the description lacks behavioral details such as whether the tool modifies data, required permissions, rate limits, or side effects. It only notes supported aggregation functions.

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?

Three concise sentences: purpose, examples, and alternative. Every sentence adds value; no redundancy.

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 the complex schema with 4 nested parameters and no output schema, the description is too brief. It does not cover parameter usage, return format, or additional context needed for correct invocation.

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 parameter coverage is 0% and the description does not explain the nested parameters (filter, compute, groupBy, options). It only mentions aggregation types but not how they map to the compute array.

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 computes statistics and aggregations on logs with specific examples (e.g., 'how many errors per service'), and distinguishes itself from sibling tool search-logs, which is used for actual log content.

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 (aggregation scenarios) and when-not-to-use (for log content, use search-logs). It also implies it is for logs vs other data types by naming.

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