aggregate_logs

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

With no annotations, the description carries the burden of behavioral disclosure. It explains the core behavior (computing metrics, supporting group-by) but does not address return format, time-range handling, potential limitations, or edge cases. It adds some value beyond the schema but omits deeper behavioral traits.

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 two sentences with no unnecessary words. It front-loads the core action and mentions key capabilities concisely. Every sentence serves a purpose.

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 description is adequate but not thorough. It introduces the tool's purpose and key features, but with no output schema, it does not explain the structure of results (e.g., how timeseries or groups are returned). Given the complexity of aggregation parameters, a bit more context about the return value would improve completeness.

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 input schema provides 100% coverage of all parameters with detailed descriptions, enums, and defaults. The tool description essentially restates what the schema already documents (e.g., aggregations, group-by). It adds no new parameter-level meaning, so the baseline of 3 applies.

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 specific function: aggregating Datadog logs to compute metrics like count, avg, sum, min, max, and percentiles. It also distinguishes itself from sibling tools (search_logs, get_log) by focusing on aggregation rather than retrieving individual logs or raw search results.

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 for when metrics need to be computed from logs, but it does not explicitly state when to use this tool instead of search_logs or get_log. It lacks explicit alternative guidance or exclusion conditions, so usage context is only implied.

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