generate_quality_report

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

No annotations are provided, so the description carries the full responsibility. It discloses what the tool does and what the output contains, which is sufficient for a zero-parameter dashboard generator. It does not mention potential side effects or prerequisites, but these are unlikely to be significant.

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 concise sentences with no filler. The first sentence states the core action, and the second enumerates the outputs. Both sentences add 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 zero-parameter tool with no output schema, the description is highly complete. It tells the agent exactly what the tool does and what the results contain, leaving no ambiguity about invocation or expected response. Minor gap: no explicit mention of when to use vs. sibling tools, but that's already accounted for in usage guidelines.

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 tool has zero parameters, and the description correctly omits parameter details. Based on the rubric, a baseline of 4 is appropriate for tools with no parameters.

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 identifies the tool as generating a quality dashboard for all validated agents, listing specific output components (per-agent summaries, pass rate, performers, recommendations). This distinguishes it from sibling tools like validate_output or get_failure_patterns, which focus on individual validation or failure 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 this tool is for obtaining a holistic quality overview after validation, but it does not explicitly contrast it with alternatives or state when not to use it. However, the output details make the intended usage clear enough.

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