fleet_verdict

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions parallel execution and the return structure (per-sensor results and fleet summary), but lacks details on performance characteristics (e.g., timeouts, rate limits), error handling, or side effects. For a tool that likely involves significant computation, this leaves critical behavioral traits undocumented.

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 and front-loaded, consisting of two sentences that efficiently convey the core action and output. There is no wasted text, and it avoids redundancy, though it could be slightly more structured by explicitly separating purpose from output details.

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 complexity of running performance verdicts across multiple sensors, the lack of annotations, and no output schema, the description is incomplete. It doesn't explain the format of 'per-sensor results' or 'fleet summary', potential errors, or dependencies on other tools like 'discover_sensors'. This leaves significant gaps for an AI agent to understand the tool's full context.

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%, with both parameters ('build' and 'profile') clearly documented in the schema. The description adds no additional parameter semantics beyond what the schema provides, such as examples or constraints on values. This meets the baseline score of 3 since the schema does the heavy lifting.

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 with specific verbs ('run performance verdict') and resources ('all discovered sensors'), and it specifies the parallel execution mode. However, it doesn't explicitly differentiate from the sibling tool 'sensor_performance_verdict', which appears to be a similar single-sensor version, leaving some ambiguity about when to choose one over the other.

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 no guidance on when to use this tool versus alternatives, such as the sibling 'sensor_performance_verdict' tool. It mentions running against 'all discovered sensors' but doesn't clarify prerequisites (e.g., whether sensors must be discovered first via 'discover_sensors') or contextual constraints, offering minimal usage direction.

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