mutation_view

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does an excellent job of stating exactly what will be rendered: magenta sticks for mutated residues, grey sticks for nearby residues, grey cartoon for backbone, and yellow sticks for ligands. It also documents the mutation format and side-chain representation. However, it does not explicitly state whether this command mutates existing view state or creates a new object, nor does it mention error handling for invalid mutation strings, which prevents a perfect score.

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 well-structured and appropriately sized. The first sentence states the core purpose, followed by a compact but complete paragraph detailing visual styling, and a separate 'Mutation format' section for syntax. There is no redundant prose; every sentence contributes necessary operational information.

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 visualization tool with a rich output schema (indicated by context signals) and full schema parameter coverage, the description is remarkably complete. It specifies input format, examples, rendering rules for mutated and nearby residues, ligand highlighting, and structural context. Nothing critical is missing for an agent to invoke this 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?

The schema already provides descriptions for both parameters ('obj_name' and 'mutations'), so schema coverage is 100%. The description adds substantial semantic value by detailing the mutation notation ('<wildtype_aa><resi><mutant_aa>'), giving concrete examples, explaining the optional chain prefix format ('A:A123G'), and describing how the mutations parameter maps to the visual output. This goes beyond the schema's basic descriptions.

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 clear verb+resource statement: 'Highlights mutated residues on the protein structure.' It then enumerates the specific visual representation (magenta sticks, labels, nearby residue context, cartoon backbone, ligand rendering), which fully distinguishes it from sibling view tools like conservation_view or bfactor_view.

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 makes the primary usage context obvious: it is for visualizing a specified list of mutations on a protein structure. It provides detailed input format examples ('A123G,V45L,T200S') and optional chain prefixes, giving clear practical guidance. However, it does not explicitly discuss when not to use this tool or mention alternatives such as conservation_view or mutation-specific analysis tools, 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.