get_outliers_api_validation_outliers_all__pdb_id__get

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

Annotations already provide readOnlyHint=true, so the description adds behavioral context about grouping by category and per-residue types_of_outliers, which goes beyond the annotations. No contradictions.

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 relatively concise, providing a clear list of outlier categories in one sentence. However, there is a missing period and the title in annotations duplicates the description, reducing conciseness slightly.

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, the description adequately informs about output structure (grouping, unit-id notation, types_of_outliers). However, it lacks details on pagination, ordering, or exact JSON format, leaving some gaps for a comprehensive 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?

Schema coverage is 100% with a single parameter pdb_id described as '4-character PDB id code'. The description does not add additional parameter semantics beyond what the schema provides, so baseline 3 is appropriate.

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 lists all geometric and model-data outliers detected by MolProbity, grouped by category, with specific examples. The tool name itself indicates 'outliers_all', and the description distinguishes from sibling summary tools by providing detailed output.

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 usage for detailed outlier information when summary stats from sibling tools are insufficient. However, it does not explicitly state when to use this tool vs alternatives like get_summary_quality_scores.

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