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

The description adds behavioral context beyond the annotations by specifying that the tool uses GitHub PR and Snyk data to produce confidence scores. However, it does not disclose potential limitations like model update frequency (given openWorldHint), authentication requirements, or performance characteristics. The annotations already indicate the tool is read-only, idempotent, and open-world, so the bar is lower, but the description could still elaborate on these aspects.

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 at four sentences, with the main purpose front-loaded. It efficiently covers the tool's function, data sources, inputs, outputs, and ideal use case without unnecessary detail. Minor improvement could be to structure information more obviously (e.g., separate inputs and outputs), but it is already well-organized.

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 tool's complexity (5 parameters, output schema exists), the description is fairly complete: it covers the use case, data sources, input examples, and output structure. It does not explain parameter interactions (e.g., whether incident_id and pr_url are mutually exclusive) or the async behavior in this specific context, but these are minor gaps. The output schema handles return values, so the description does not need to detail them.

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 description mentions inputs (GitHub PR URL or incident ID) but does not explain all parameters. For example, time_range_days, snyk_org_id, and async are not described in the description, even though the schema has low coverage (20%). The description compensates partially by indicating the use of Snyk data (linking to snyk_org_id) but leaves gaps. Baseline is 3 for low coverage, and the description only elevates slightly by clarifying purpose of pr_url and incident_id.

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 classifies root causes of change failures for CTO-level incident analysis, specifying inputs (GitHub PR metadata, Snyk vulnerability data) and outputs (structured categories with confidence scores). However, it does not explicitly distinguish itself from sibling tools that may also analyze change failures, such as dependency_vulnerability_scan or code_review_depth_optimizer.

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 usage context ('Ideal for post-mortem analysis and change risk assessment') but does not specify when not to use the tool or suggest alternatives among sibling tools. There is no guidance on conditions like insufficient data or specific incident scenarios where other tools would be more appropriate.

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