qscore_view

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

With no annotations provided, the description carries the full transparency burden. It discloses several behavioral traits: no network/map/computation, reads published numbers, and warns about pre-2023 validation reports lacking Q-scores and Q-scores going negative. However, it does not mention that the tool overwrites B-factors, which is implied by the preserve_bfactors parameter and is a meaningful side effect.

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 concise and well-structured, leading with the primary purpose and then adding useful caveats in a clear list. Every sentence contributes meaningful information without fluff or redundant restatement of the tool name.

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?

The description provides substantial context for a tool with full schema coverage and an output schema. It explains data expectations and edge cases (older entries lacking Q-scores, negative values) that a user would need to interpret results correctly. The main gap is the unmentioned B-factor overwrite behavior, but overall the description plus schema gives an agent enough to use the tool correctly.

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 100%, so the schema already explains all parameters. The description does not add parameter-level meaning beyond the schema; it only repeats that the validation report is parsed. This meets the baseline for a fully documented schema but provides no extra semantic value.

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 opens with a specific verb and resource: 'Colour a model by per-residue Q-score parsed from its wwPDB validation report.' It clearly distinguishes this from sibling visualization tools by emphasizing that Q-scores are read from a validation report, not computed or fetched from the network.

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 gives clear context for when to use this tool: 'No network, no map, and no computation — Q-scores are already published, and this reads the numbers rather than re-deriving them.' It implies when this is appropriate, though it does not explicitly name sibling alternatives or state when-not-to-use conditions.

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