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 value by listing the categories of information returned, but does not expand on auth needs, rate limits, or output format beyond those categories.

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?

A single, front-loaded sentence with no filler. It states the action, scope, and returned content efficiently.

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 read-only tool with complete parameter documentation and annotations that cover safety, the description sufficiently explains what the tool returns. It could be slightly stronger by clarifying when to prefer it over list_tools or get_workflow_tool, but the core behavior is clear.

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%, and the single parameter's meaning is fully documented in the schema. The description does not add syntax, format, or additional meaning beyond what the schema already provides, so the baseline of 3 is appropriate.

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 uses a specific verb ('Inspect') and resource ('one ProteinIQ tool'), and lists the data returned (accepted inputs, settings, outputs, credit model). It implicitly distinguishes itself from list_tools by focusing on a single tool, though it does not explicitly name alternative siblings.

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?

Usage is implied: the tool retrieves details for a specific tool, likely after listing tools. However, there is no explicit when-to-use guidance, no when-not-to-use exclusions, and no named alternatives such as list_tools or get_workflow_tool.

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