get_coverage

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

With no annotations provided, the description carries the full burden. It discloses the prerequisite, the JSON return structure, and the three main components (coverage entries, gap analysis, suggestions). It does not state what happens when the prerequisite is unmet, but for a read-only lookup this is reasonably complete.

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 three sentences and front-loads the core function. The final sentence restates the purpose somewhat redundantly, but overall the length is appropriate for the complexity.

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 and no parameters, the description compensates well by spelling out the return shape and prerequisite. The overlap with suggest_tests is not directly addressed, leaving minor ambiguity about when to prefer this over that sibling.

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)Skip baseline 4 applies since the description does not need to compensate for missing parameter documentation.

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?

States a specific verb ('Return'), a clear resource ('test coverage map'), and the additional outputs ('gaps' and 'suggested tests'). Despite the sibling suggest_tests, the description clearly frames this as a coverage-status tool rather than a test-generation tool.

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?

Explicitly names the prerequisite (scan_codebase must have been called first) and gives a direct use case ('Use this to understand what has been tested and what still needs testing'). It does not name alternatives or exclusions, but the context is clear enough for an agent to select it.

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