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

Despite sparse annotations (only hints, no safety profile), the description discloses important behavioral traits: daily limit (20 per account), size cap (32 KB), redaction of credential-like values, and the return shape (request_id and Studio URL) plus how replies are delivered. This goes well beyond the annotations and helps the agent understand side effects and expectations.

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 description is a single dense paragraph that front-loads the primary purpose and then delivers essential behavior and return info. It is efficient—no filler—though it could be split into sections for readability. Every sentence adds value; it is concise but rich.

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?

Given the tool's complexity (9 parameters, 3 required, no output schema), the description covers the key operational aspects: what to include, how to track, limits, and return. The schema handles parameter details, so the description need not restate them. It is complete enough for an agent to call correctly and understand the outcome.

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 78%, with most parameters already described. The description adds meaningful usage context: it instructs that reproduction should be a runnable exact tool call or curl, what to put in expected and acceptance, and clarifies that body is free text with markdown (already in schema but reinforced). It does not leave any critical parameter ambiguous, and it adds practical fill-in guidance.

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 states a specific verb ('open a request'), a resource ('Fine Structure platform team'), and the scope (platform issues, not user's app). It explicitly lists the three request types (bug, feature, question) and distinguishes from user-app issues, so an agent can clearly tell what this tool is for.

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 clear trigger condition ('when something in the platform blocks you instead of working around it') and explicitly excludes user-app issues. It also explains the workflow for tracking via get_platform_request / list_platform_requests, and what to include in the body. This is decisive guidance for when to use this tool.

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