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

With no annotations, the description carries the full load and does disclose key traits: it executes live through the same checker (so behavior mirrors the real tool), it costs nothing, and the input is a fixed fictional fintech with deliberately weak answers. It omits latency/caching behavior for a live run, which is the main remaining gap.

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?

Two tight sentences, front-loaded with the action and the output, then the cost and scope caveat. Every clause carries information; nothing is filler.

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 zero-param tool with no output schema, the description tells the agent what comes back (verdict + top findings) and that no input is required. The shape of 'top findings' is left unspecified, a minor gap given no output schema exists.

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?

Zero parameters, so the baseline is 4. The description goes slightly beyond by explaining why there are none — the intake is a fixed published sample — which prevents an agent from hunting for a way to pass its own data.

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?

Names a specific action (run the published sample intake live through the same checker) and states the return (verdict + top findings). The clause 'this is the sample, not your architecture' implicitly distinguishes it from sanity_check_run, though it never names the sibling directly.

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?

'Free preview' gives a clear condition for choosing this tool, and 'this is the sample, not your architecture' is an effective when-not signal that steers agents to the real-run tool. No sibling is named explicitly, so routing relies on inference.

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