validate_structure_quality

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool validates and assesses quality, implying a read-only analysis, but doesn't describe what 'validate' entails (e.g., checks for errors, compares to standards), what 'assess' outputs (e.g., scores, reports), or any constraints like rate limits or authentication needs. This leaves significant gaps for a tool with no annotation coverage.

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, efficient sentence that directly states the tool's purpose without any fluff or redundancy. It is appropriately sized and front-loaded, with every word contributing to understanding the tool's function.

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 complexity of validating and assessing prediction quality, the lack of annotations and output schema, and the presence of many sibling tools, the description is incomplete. It doesn't explain what 'quality' means in this context, what the output includes (e.g., scores, validation reports), or how it differs from related tools, leaving the agent with insufficient information to use it effectively.

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 has 100% description coverage, with the single parameter 'uniprotId' documented as 'UniProt accession'. The description adds no additional meaning beyond this, such as explaining how the UniProt ID maps to the AlphaFold prediction or any format specifics. With high schema coverage, the baseline score of 3 is appropriate as the schema does the heavy lifting.

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 action ('validate and assess') and the target resource ('overall quality of an AlphaFold prediction'), providing a specific verb+resource combination. However, it doesn't distinguish this tool from its many siblings (like 'analyze_confidence_regions' or 'get_confidence_scores'), which might also assess prediction aspects, so it doesn't reach the highest clarity level.

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 provides no guidance on when to use this tool versus alternatives. With 18 sibling tools, including ones like 'get_confidence_scores' or 'compare_structures' that might overlap in assessing prediction quality, the lack of explicit when/when-not usage or named alternatives leaves the agent without clear direction.

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