conservation_view

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

With no annotations provided, the description carries the full burden and does an excellent job. It discloses the external MMseqs2 API call, potential 30-second-to-minutes latency, in-memory caching by sequence, the effect on B-factor and spectrum coloring, and the color-mapping semantics. This is far beyond minimal and gives the agent a realistic expectation of side effects and performance.

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 paragraph-structured but each sentence earns its place: purpose, pipeline, caching, color mapping, and a latency note. It is front-loaded with the primary function, remains readable, and avoids 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?

Given the tool's complexity (external API, caching, B-factor modification) and the existence of an output schema, the description covers all critical aspects: the full pipeline, the color interpretation, performance expectations, and caching behavior. It leaves no major operational gaps.

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% with detailed parameter descriptions, providing a baseline of 3. The description adds value by explaining that changing the scale does not trigger a new API call and that re-runs are cached, which directly informs understanding of 'scale', 'force_refresh', and 'server_url'. This extra context justifies a score above baseline.

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, specific verb+resource statement: 'Colors the structure by evolutionary conservation using Shannon entropy.' This explicitly distinguishes it from sibling view tools like bfactor_view or plddt_view, and the detailed pipeline further clarifies its unique function.

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 provides clear context on when to use the tool (for evolutionary conservation coloring) and includes practical usage guidance about the first-call latency and caching. However, it does not explicitly name alternatives or state when not to use it, so it stops short of full exclusion guidance.

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