design_sequences

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

With no annotations the description carries the full burden, and it partially fulfills it by disclosing the underlying model (ProteinMPNN), the return payload (mfasta and scores), and the score direction (lower is better). It is silent on cost, determinism, input size limits, and whether designs are sampled stochastically, which are relevant behavioral traits for a generative design tool.

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 summary sentence is front-loaded and the Args/Returns blocks are compact and scannable. Slightly verbose in listing parameters that also appear in the schema, but no sentence is wasteful.

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?

Output semantics are covered by both the description and the output schema, and both inputs are explained. However, for a generative design tool with no annotations, the description should say more about resource cost, determinism, or constraints on the backbone input to fully guide an agent.

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 0%, so the description must compensate, and it does: input_pdb is defined as PDB text of the backbone and num_sequences as the number of candidates. This meaningfully clarifies both parameters, though it omits the default of 8.

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 (Design) and resource (amino-acid sequences), and pins down the mechanism (ProteinMPNN) plus the input requirement (a given backbone). This clearly differs from the sibling design_backbone or fold_complex, but it never names those siblings, so differentiation is implicit rather than explicit.

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 says what the tool does but gives no when-to-use guidance, no prerequisites, and no comparison to siblings like design_binder or design_backbone. An agent must infer that this is the sequence-design step rather than getting explicit routing.

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