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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, so the safety profile is covered. The description adds behavioral context by disclosing the data sources (DORA metrics, GitHub PRs), the input requirements, and the nature of the output (structured recommendation with supporting metrics and risk-adjusted rationale). No contradictions with annotations.

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, front-loaded with the tool's core purpose and ending with the output format. The first sentence is slightly long but every clause adds useful detail. There is no fluff or redundancy, though it could have been trimmed without losing meaning.

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 presence of an output schema, full parameter descriptions, and strong annotations, the description provides sufficient context for selection and invocation. It covers the tool's inputs, data sources, and output style. It does not explain optional parameter behavior or use cases, but these are already documented in the schema, and the tool is not overly complex.

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%: all parameters have descriptions. The description reiterates 'repository identifier and time range' but adds no new meaning beyond the schema. It mentions output categories (light, standard, thorough) but does not explain how parameters like teamSize or riskTolerance affect results, so a 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 states the tool's purpose: analyzing DORA metrics and GitHub PR data to recommend an optimal code review depth. It specifies the verb ('analyzes'), the resource ('historical DORA metrics and GitHub pull request data'), and the output ('a structured recommendation on review rigor'), making it distinguishable from sibling tools like dora_metrics_deep_dive.

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 gives implied usage context ('As a CTO... Input your repository identifier and time range') but does not explicitly state when to use this tool versus alternatives such as dora_metrics_deep_dive or change_failure_root_cause_classifier. There are no exclusions or alternative-tool references, so guidance exists only at an implied level.

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