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

Annotations already establish that this is a read-only, non-destructive, closed-world operation. The description adds meaningful behavioral context beyond that: it discloses the iterative scoring approach, that parental CDRs are kept by default, that a Kabat Vernier option exists, and that the output reports every mutation plus the next model-proposed changes. It also cites validation against the published PyTorch model and ANARCI's PDB benchmark, which helps the agent trust the behavior.

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 three sentences and front-loads the core action and algorithm. The second and third sentences add output-reporting and validation context without wasting words, though the validation sentence is somewhat promotional relative to invocation needs.

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 rich input schema, an output schema, and annotations, the description covers the essential mechanism, defaults around CDR preservation, and what the tool reports. It does not need to restate return values, but it could more explicitly say when this humanization approach is preferable to alternatives.

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%, and each parameter already carries a description, so the baseline is 3. The description adds conceptual meaning around frameworks, parental CDRs, and the Kabat Vernier zone, but it does not explain parameters such as domain selection, iterations, or the cdrDefinition enum beyond what the schema already provides.

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 gives a specific verb and resource: humanize an antibody variable domain using Sapiens. It explains the mechanism (score positions against human repertoires, keep parental CDRs, optional Vernier preservation), which clearly distinguishes it from grafting-only siblings such as graft_antibody_cdrs, though it never names an alternative explicitly.

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 implies the tool's use case through its detailed humanization workflow, but it gives no explicit when-to-use, when-not-to-use, or sibling comparison guidance. An agent can infer this is the humanization tool rather than a numbering or grafting tool, but must make that inference without direct routing advice.

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