severity_summary

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

With no annotations, the description carries the full burden. It discloses meaningful behavior by detailing exactly what the tool returns, including item counts, breakdowns, and prioritized feedback IDs. It does not explicitly state that it is read-only, but 'summarize' and 'returns' strongly imply no mutation, so this is a minor omission.

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 the core purpose front-loaded in the first sentence and the output details packed efficiently into the second. Every sentence adds value and there is no redundant or filler text.

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?

For a zero-parameter aggregation tool with an output schema, the description is quite complete: it states scope and enumerates the returned information. It could additionally say when to use this instead of search_feedback or tag_severity, but that gap is largely covered by the visible sibling context and the clear aggregate nature of the tool.

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 has zero parameters, so there is nothing for the description to clarify. The description's 'whole dataset' confirms the tool takes no filtering scope, which is consistent with the empty properties object. Baseline 4 is appropriate for a no-parameter tool.

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 opens with a specific verb and resource: 'Summarize severity across the whole dataset.' It then enumerates concrete outputs (counts by severity level, source/customer breakdowns, top ten IDs), making it unmistakable what this tool does and distinguishing it from sibling tools like search_feedback or tag_severity.

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 phrase 'whole dataset' implies this is for a high-level overview rather than for searching individual feedback, tagging severity, or listing themes. However, it never explicitly states when to prefer this tool over its siblings or mentions any exclusions, so the 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.