design_binder

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

No annotations are provided, so the description carries the burden. It usefully discloses the underlying models (RFdiffusion, ProteinMPNN) and the return keys (backbone_pdb, designs, scores), but omits operational traits like GPU cost, expected runtime, or whether the call is expensive/long-running — important for a diffusion pipeline.

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?

Three short sentences with zero filler, front-loaded with the core pipeline composition, then return shape, then the recommended next step. Every sentence earns its place.

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?

An output schema exists, so return values need not be re-explained (though the description does list keys). For a multi-parameter pipeline tool with 0% schema description coverage, the description leaves the agent without guidance on the two required parameters, which is a meaningful gap despite adequate workflow framing.

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 0% across four parameters (contigs, target_pdb, hotspot_res, num_sequences), and the description explains none of them. There is only an oblique reference to 'the target', which does not clarify contig syntax, hotspot residue format, or the meaning of num_sequences.

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?

States a specific verb and resource ('design_binder' = design a binder) and explains that it is a pipeline combining RFdiffusion backbone generation with ProteinMPNN sequence design 'in one call'. This implicitly differentiates it from the design_backbone and design_sequences siblings, though it never names them.

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?

Gives clear usage context: the pipeline nature implies you use this instead of chaining the two single-step tools. It also explicitly names the follow-up tool and why ('Fold the designs against the target with fold_complex to score binding'), which routes the agent through the workflow. No explicit when-not guidance.

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