plddt_summary

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

No annotations are provided, so the description carries the full behavioral burden. It does disclose a genuinely useful guard (refusing experimental B-factors) and the shape of its output (mean, bands, low-confidence regions), but it does not say what 'refuses' looks like operationally, nor any error or permission behavior.

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?

Three short sentences, front-loaded with the core action and immediately followed by output content and the key constraint. No filler or redundancy.

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?

For a single-parameter tool with an output schema, the description is nearly sufficient: the output schema covers return values, and the description covers input source and the experimental-structure guard. The one notable omission is any reference to the near-duplicate sibling tool.

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 description coverage is 0% and there is a single required parameter 'pdb_path'. The description implies the input is a PDB structure whose B-factor column holds pLDDT, which adds some meaning, but it does not clarify accepted formats, path expectations, or whether AlphaFold PDB files are required.

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 names a specific verb and resource ('Summarize AlphaFold-style pLDDT confidence from B-factor columns') and even states the data source, so an agent understands exactly what it produces. However, it never differentiates itself from the sibling tool 'alphafold_plddt', whose near-identical scope could cause mis-selection.

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 refusal clause 'Refuses to interpret experimental-structure B-factors as pLDDT' gives an implicit when-not-to-expect-results condition, which is useful. But there is no explicit when-to-use guidance and no routing to the obvious alternative 'alphafold_plddt' or 'structure_stats', leaving selection to inference.

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