Critique Report

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds meaningful context about the tool's purpose—transparency into the validation layer—which helps the agent understand the intent and expected output. No contradiction 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?

Two sentences with zero fluff. The action is front-loaded ('Show exactly which findings...') and the rationale follows immediately. Every word earns its place.

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 read-only tool with one parameter and no output schema, the description is fairly complete. It conveys what the tool does and why. However, it does not specify the format or granularity of the output (e.g., how many findings, structured vs. textual), which could leave slight ambiguity for an agent expecting precise output shape.

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 single parameter 'path' is already fully described in the schema (100% coverage). The tool description adds no additional meaning or usage details about the parameter, so the baseline score 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 states a specific verb ('Show') and resource ('which findings were rejected as false positives'), and explains the rationale ('verify ARGUS's own accuracy'). This clearly distinguishes it from sibling tools like verify_all or audit_360, which focus on different aspects of 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 implies a use case (verifying the validation layer's accuracy) but does not explicitly state when to use this tool versus alternatives, nor does it mention any exclusions or preconditions. Usage context is implied rather than explicit.

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