get_outliers_api_validation_outliers_all__pdb_id__get

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

The description adds behavioral context beyond the readOnlyHint annotation, such as reporting outliers using unit-id notation and summarizing types_of_outliers per residue. It also lists specific outlier categories, giving the agent a clear picture of what the tool provides without contradicting annotations.

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, well-structured sentence that conveys all necessary information efficiently. It is front-loaded with the main purpose and includes key details. Slightly longer than ideal but still concise.

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?

With no output schema, the description provides sufficient detail about the output structure (grouping by category, unit-id notation, types_of_outliers section). It covers the main aspects needed for an agent to understand what the tool returns.

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 only parameter (pdb_id) is fully described in the input schema with the same explanation. The description does not add new semantic meaning for the parameter, so it meets the baseline but does not exceed it.

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 that the tool lists geometric and model-data outliers detected by MolProbity, grouped by category. It lists specific outlier types (clashes, sidechains, RSRZ, etc.), making the purpose highly specific and distinguishing it from sibling tools that handle other types of PDB 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?

The description does not explicitly state when to use this tool versus alternatives. While the sibling tools are for different data types, the description provides no guidance on context or exclusions. The purpose is clear but usage guidance is limited.

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