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

Annotations already cover the safety profile (readOnlyHint=true, openWorldHint=false, destructiveHint=false). The description adds a genuinely useful claim beyond them: results come from a real algorithm engine rather than LLM generation, so values are deterministic and trustworthy for reporting. It does not discuss sequence validity constraints (e.g. non-standard residues) or error behavior, keeping it short of a 5.

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?

Four compact segments: a bracketed topic tag, the use case, the return values, and the engine provenance. It is front-loaded and wastes little, though the bracketed tag restates the title and is mildly redundant.

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 simple two-parameter, single-purpose calculation tool with a full-coverage schema and no output schema, the description supplies what an agent needs: what it computes, what comes back, and that the numbers are algorithmically derived. Nothing essential is missing.

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 100%, so both parameters (sequence, optional protein_name) are already documented and the baseline applies. The description only echoes that a protein sequence is the input and adds no format, length, or single-letter-code conventions for the sequence field.

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 names a specific computation (protein physicochemical properties) and enumerates the concrete outputs: molecular weight, isoelectric point, hydrophobicity (MW/pI/GRAVY). That output list is what distinguishes it from nearby siblings such as act_protein_digest, act_protein_ptm and act_sequence_translate, though those siblings are never explicitly named.

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?

It gives a usable trigger — the user supplies a protein sequence and wants MW/pI/GRAVY — which is enough to route an agent here. There is no when-not guidance and no mention of the alternative protein/sequence tools, so disambiguation is left to inference from the listed outputs.

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