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

Annotations already cover read-only, idempotent, non-destructive and closed-world, so the description only needs to add. It does: no GPU, executed inline on every request, credential scope required, and the notable caveat that 'answers released: false while it is not switched on for your workspace.' That caveat is genuinely useful context an agent wouldn't get from annotations, though rollback/persistence behavior of stored versions is left vague.

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 core purpose is front-loaded, but the middle is a breathless run-on stacking jargon ('across every vertical, pricing topology, unit, buyer and channel') and the fragment 'Answers released: false while it is not switched on for your workspace' reads awkwardly. Tightening into two clean sentences would preserve all meaning at half the length.

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?

With no output schema, the description carries the burden of describing returns, and it does: version identifier, profit given up on one-look eval markets (with direction stated, 'lower is better'), stored versions, and the released flag. Auth requirements are also covered. Only the shape of 'stored versions' and how eval profit is measured remain unspecified.

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?

The tool takes zero parameters, so the baseline is 4. The description adds no parameter meaning because none is needed, and the empty schema with additionalProperties=false is fully self-explanatory.

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?

Opens with a specific verb+resource ('Read the frontier price model') and immediately characterizes what that model is (a network trained on simulated markets, run on every request with no GPU). It is clearly distinct from siblings like get_learned_pricing or suggest_frontier_price. The dense run-on phrasing ('every vertical, pricing topology, unit, buyer and channel') adds noise but doesn't obscure the core purpose.

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 only implied: you read this to see which version runs, what profit it gives up, and whether it's released for your workspace. There is no explicit when-to-use/when-not guidance and no mention of alternatives such as suggest_frontier_price or get_learned_pricing, so an agent must infer the choice. The prerequisite (Last Price credential with routing read scope) is stated but that is a gate, not a usage rule.

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