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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds real context beyond that: it explains the exclusivity failure mode ('sending `scaleId` together with `kind` is refused by name rather than one silently winning'), the normalisation/weighting behaviour, and that output is one value per window with its centre residue. It doesn't mention compute cost or limits, which keeps 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 sentences, front-loaded with the operation and the two routes into it, with no filler. The long enumerations of scales and scale IDs are correctly pushed into the schema rather than bloating the prose, though the second sentence is dense enough to require a second read.

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 10-parameter tool with enums, nested `_subject`/`_files` objects, an output schema, and full annotation coverage, the description supplies the conceptual map (two exclusive input modes, normalisation, windowing, per-window return) that lets an agent call it correctly. Nothing critical is missing, though it could be slightly more explicit about the scaleId-vs-kind decision being enforced by the server.

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 the schema already documents every parameter including the enum value lists and the per-parameter caveats (e.g. `window` ignored for flexibility, `normalize` requires `scaleId`). The description mostly restates these and adds only the framing that `kind` selects among five classic profiles, so the baseline 3 for a fully-covered schema is correct.

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 up front ('Windowed physicochemical profile along a protein') and identifies the underlying engines (ExPASy ProtScale, ProtParam profiles), so the agent knows it is computing per-window physchem curves rather than bulk protein properties. However, it never names or contrasts itself with close siblings such as protein_properties, protein_membrane_profile, or protein_disorder, leaving differentiation to inference.

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 lays out two clear input paths — `scaleId` for the 57 ProtScale scales plus 14 Biopython tables, `kind` for the five ProtParam profiles — which tells the agent when each branch applies. It does not state any condition for choosing this tool over its neighbours, and the mutual-exclusivity guidance lives largely in the schema rather than the description.

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