sensor_trend

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool is 'useful for spotting when regressions were introduced,' which hints at analytical behavior, but doesn't describe output format, data structure, performance characteristics, or potential side effects. For a tool with no annotations, this is insufficient to inform an agent about how the tool behaves beyond its basic purpose.

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 extremely concise and well-structured, consisting of two sentences that directly state the purpose and utility. The first sentence clearly defines what the tool does, and the second adds context without redundancy. Every sentence earns its place, making it front-loaded and efficient.

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 (a performance analysis tool with 2 required parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the output looks like (e.g., a chart, table, or metrics), how results are formatted, or any limitations. This makes it inadequate for an agent to fully understand how to interpret the tool's results.

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 input schema has 100% description coverage, with clear parameter descriptions in the schema itself (e.g., 'Sensor type (e.g., "AP3000", "AP5000")'). The tool description adds no additional parameter semantics beyond what's already in the schema. According to the rules, when schema_description_coverage is high (>80%), the baseline score is 3 even with no param info in the description, which applies here.

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: 'Show a sensor type's performance across all builds in baselines.json.' It specifies the verb ('show'), resource ('sensor type's performance'), and scope ('across all builds in baselines.json'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'sensor_performance_verdict' or 'explore_sensor_metrics', which prevents a perfect score.

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 implied usage guidance with 'Useful for spotting when regressions were introduced,' suggesting it's for regression analysis. However, it lacks explicit guidance on when to use this tool versus alternatives (e.g., 'compare_builds' or 'sensor_performance_verdict'), and doesn't mention prerequisites or exclusions. This leaves room for ambiguity in tool selection.

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