superposition_view

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

With no annotations, the description carries full responsibility, and it delivers thoroughly. It discloses the coloring gradient (blue-white-red), that the target is left grey, residue pairing logic by chain and number with fallback, and the multimer pitfall with a specific numeric illustration (4AKE vs 1AKE). It also explains the meaning of max_deviation in the workflow. No annotation contradiction exists.

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 appropriately sized for its complexity. Each sentence adds necessary information: the core purpose, the rationale versus RMSD, the coloring mechanism, usage scenarios, pairing rules, and crucial caveats. It is well-structured, starting with a clear statement and then expanding into practical details without 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?

The tool is complex with visual output and multiple parameters, but the description covers all essential aspects: the operation, coloring logic, pairing behavior, prerequisites, and edge cases. It even gives a concrete example to illustrate the multimer issue. An output schema exists, so return-value documentation is not required. The description is complete enough for an agent to use the tool correctly and safely.

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%, so the baseline is 3. The description adds value beyond the schema by explaining the max_deviation behavior (defaults to largest observed shift, set explicitly to compare pairs) and implying how mobile/target relate via the superposition concept. However, it does not fully elaborate on method choices; still, the schema already provides clear field descriptions, so the added semantic context is a bonus.

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-resource combination: 'Superposes two structures and colors the mobile one by per-residue shift.' It clearly distinguishes this from sibling tools by explaining that it provides per-residue visualization rather than just an RMSD number, and the explicit mention of 'mobile onto target' and coloring scheme establishes a unique identity.

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 explicitly states when the tool is best used: 'Best on two states of the same protein — apo vs holo, open vs closed, a mutant against wild type.' It also provides critical practical guidance: both structures must be loaded, warns about fetch_structure clearing the session, recommends replace=False, and cautions against multimers with a concrete example. This goes beyond simple context to include exclusions and workarounds.

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