ensemble_spread_view

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

With no annotations, the description carries full responsibility for behavioral disclosure. It thoroughly explains the computation (per-residue RMS deviation), the effect (B-factor column overwritten, blue-to-red spectrum coloring, tube thickness), and the interpretive caveat. This goes well beyond minimal disclosure.

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 four sentences long, front-loaded with the main action followed by technical detail and a caveat. Every sentence earns its place: the action, the computation, and the crucial interpretation warning. No redundancy or filler.

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 tool has moderate complexity, and the description covers the computation, visual output, and scientific interpretation. It also includes a necessary caution about the metric's meaning. While it does not mention restoring B-factors, it provides enough context for an agent to select and invoke 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 both parameters. The description adds context about the overall behavior but does not elaborate on parameter syntax or semantics beyond the schema. 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 opens with a specific verb and resource: 'Colour and thicken a multi-state object by how much its states disagree.' This clearly identifies the tool's action and target, and distinguishes it from siblings like conservation_view or bfactor_view by focusing on inter-state disagreement.

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 clearly implies the tool is for multi-state objects and explicitly states what the spread is NOT (calibrated uncertainty/error bar), providing a when-not-to-use caution. However, it does not name alternative tools or explicitly explain when to choose this over a sibling, so it falls short of a 5.

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