coverage_suggest_tests

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

There are no annotations, so the description has the full burden of behavioral disclosure. It only states the intent and never mentions output format, auth requirements, side effects, or limits. This is a thin description for a tool with no structured metadata beyond the schema.

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 one front-loaded sentence with no redundant filler. It is efficient for what it conveys, though it leaves behavioral details for other dimensions.

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 simple suggestion tool, the core inputs and action are clear and the schema covers all parameters. However, there is no output schema and the description does not state what the returned suggestions look like or when to prefer this over sibling tools, leaving it only minimally complete.

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 documents all 4 parameters with descriptions, so the baseline is 3. The description adds the relationship between gap analysis and source code but does not explain how api_key or framework influence the suggestions; the schema already covers their basic meaning.

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 function: it 'suggests specific tests to write ... based on gap analysis'. The verb-object pair distinguishes it from adjacent analysis/report tools such as coverage_analyze_gaps and coverage_report, though it doesn't explicitly name alternatives.

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?

No when-to-use guidance is provided. The required parameters imply the agent should have a gap analysis and source code, but the description never says to use this after coverage_analyze_gaps or how it differs from generic test-generation tools like test_generate_unit.

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