control_evidence

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

With no annotations, the description fully discloses behavioral traits: populationSize is the full count, sample is capped, truncated and scanTruncated flags indicate limits. It explains what happens when limits are hit, which is critical for agent decision-making.

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 well-structured: a summary line followed by bulleted argument explanations. Every sentence adds value; no fluff. It is front-loaded with the core purpose.

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 no output schema, the description adequately explains return fields (populationSize, sample, truncated, scanTruncated). It covers key constraints and edge cases. However, it could mention the output format or provide a brief example for completeness.

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 coverage is 0%, so the description compensates fully. It explains each parameter: framework with examples, control_id with placeholder, since/until as ISO timestamps, sample_size as integer. This adds essential meaning beyond the bare schema.

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 reads evidence rows for a single control, along with population size and reproducible query. It specifies the verb (READ) and distinct resource, differentiating from sibling tools that operate on multiple controls or summaries.

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 usage for fetching evidence for a single control but does not explicitly state when to use it versus alternatives like coverage_summary or gap_analysis. No when-not-to-use or alternative guidance is provided.

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