Setell learning-loop coverage

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

The description adds value beyond annotations by explaining the exact nature of the returned data (e.g., what 'maturityTier' means, the specific fields). It confirms read-only behavior and provides context that annotations alone do not, such as what 'signed quotes' and 'baseline' represent.

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, moderately long paragraph. It front-loads the purpose with a question and then lists the return fields. While efficient, it could be slightly more concise by removing the example usage in parentheses, but overall it is well-structured.

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?

Despite lacking an output schema, the description fully explains the return structure, including specific field names and meanings. It also provides usage context and examples, making it complete for agent understanding.

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 no parameters, and schema coverage is 100%. Per guidelines, baseline is 4 for zero parameters. The description does not need to add parameter details.

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 returns aggregate counts and a maturity tier summarizing the operator's data depth. It uses a specific verb ('get') and resource ('learning coverage'), and distinguishes itself from siblings by focusing on coverage metrics rather than individual entities or actions.

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 explicitly advises using this tool when the operator asks 'how much does Setell know about my pricing?' or when narrating analyst verdicts. While it does not provide explicit exclusions or alternatives, the given usage scenarios are clear and helpful.

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