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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint, lowering the bar for behavioral disclosure. The description adds valuable context about the tool's analytical behavior, such as 'deep correlation to code review patterns' and 'trend analysis,' which are not captured by the annotations. It avoids contradictions and provides insight into what the analysis encompasses.

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 concise, using four tightly written sentences that front-load the core purpose ('Analyzes DORA metrics...') and then provide context (audience, inputs, outputs). No irrelevant information or 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?

With an output schema present and full parameter descriptions, the description covers the essential aspects: purpose, target user, inputs, and outputs. It does not explain asynchronous behavior or branch defaults, but these are captured in the schema. Overall, it is complete for the tool's complexity.

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 already fully documents all parameters. The description mentions 'GitHub repository identifiers and optional time ranges' at a high level, but adds no new semantic detail beyond what the schema provides. Baseline 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: 'Analyzes DORA metrics (Deployment Frequency, Mean Time to Recovery, Change Failure Rate) with deep correlation to code review patterns.' This specific verb+resource combination distinguishes it from sibling tools like mttr_breakdown_analyzer or code_review_depth_optimizer by highlighting the integration of DORA metrics with code review analysis.

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 clear intended use: 'Designed for CTOs to identify bottlenecks in software delivery pipelines.' This gives context on when to use the tool, though it does not explicitly state when not to use it or mention alternative tools, which would elevate it to a 5.

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