prepare_coverage_gaps

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

Without annotations, the description clearly states that no LLM call happens server-side, which is a key behavioral trait. It also explains the output (spec, case titles, instructions). This adds value beyond the name.

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 a single sentence, front-loading the main purpose. It is concise but slightly run-on; could be split for clarity.

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 three parameters and no output schema, the description is incomplete. It omits details about the output format, the optional 'feature_title', and constraints on inputs (e.g., what constitutes valid 'cases').

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%, and the description only loosely implies that 'spec_text' is the spec and 'cases' are existing case titles. The 'feature_title' parameter is not mentioned at all. The description does not compensate for the lack of schema descriptions.

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 states that the tool returns the spec text, existing case titles, and instructions for coverage gap detection. It distinguishes itself by noting that no server-side LLM call occurs. However, the verb 'prepare' is vague and the core action is returning data.

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 explicit guidance on when to use this tool versus its many siblings (e.g., prepare_cases_from_text, prepare_feature_bootstrap). The description implies it's for coverage gap analysis but doesn't provide when-not-to-use or alternative contexts.

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