analyze_quality

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

The description mentions 'Returns actionable suggestions,' giving some idea of output, but with no annotations, it lacks details on side effects, permissions, or behavior in edge cases. For a read-only analysis tool, this is acceptable but not thorough.

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 concise single sentence that covers the tool's purpose and lists types efficiently. It is front-loaded and to the point, earning its place without extra fluff.

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 the tool's low complexity (single optional parameter, no output schema), the description is mostly adequate. However, it omits details like what constitutes a 'quality issue' or how suggestions are structured, leaving some ambiguity for an agent.

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 single parameter 'type' is fully described in the schema with an enum list. The description reiterates these values without adding new meaning. Baseline 3 is appropriate since schema coverage is 100% and no additional semantic information is provided.

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 analyzes Laravel code for quality issues and lists five analysis types. However, it does not explicitly differentiate itself from sibling tools like analyze_attributes or analyze_performance, missing a chance to reduce ambiguity.

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 use when a quality analysis is needed for a Laravel project but provides no guidance on when to use this tool versus alternatives, nor any prerequisites or conditions for usage.

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