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

With no annotations, the description carries the full burden and does well: it discloses price ($0.005 / 1 credit), payment rails (x402 Base USDC), latency (<50ms), billing semantics (strictly Pay-on-Proof), and output size budget (<350 tokens). It does not state whether repeat calls re-charge or how long a resolution_id remains retrievable.

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?

Cost and trigger are front-loaded, and every sentence carries operational information. Slightly dense with marketing framing ('~36x ROI', 'saving ~15,000-25,000 retry tokens'), but that framing also conveys expected value.

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 one-param tool with no output schema, the description covers cost, trigger, payment rails, and the qualitative return payload (root cause, diff, anti-patterns). An agent has everything needed to decide to call it and to predict the outcome.

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?

Only one parameter and schema coverage is 100%, so the schema baseline already applies. The description adds value by implying where resolution_id comes from ('diagnose_peek returns...'), aligning the param with the upstream 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?

States a specific verb (unlocks) and a concrete set of resources (Docker-verified root cause, surgical code diff, anti-patterns). This is clearly distinct from the sibling diagnose_peek, which only produces a confidence signal.

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?

Gives an explicit call trigger ('immediately when diagnose_peek returns VERIFIED_MATCH (confidence >= 0.85)') and an explicit non-trigger billing behavior ('$0.00 on NOT_FOUND'). The relationship to the upstream sibling is spelled out rather than implied.

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