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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint, so the description does not need to re-establish safety. What it does add beyond them is substantive: token-based billing on the hosted API, a 25-second total timeout including retries, refusal of questions lacking instructions, and the escalate contract. That is solid value beyond structured fields, but the bulk of the text serves purpose and parameter guidance rather than hidden behavior, so it sits above baseline without being exceptional.

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?

Structure is deliberate — a purpose paragraph, a use-case sentence, an explicit Inputs block, then output and cost paragraphs — and the purpose is front-loaded. But the routing use-case sentence is a long comma-spliced run, and the Inputs block re-states a fair amount that the schema already documents at 100% coverage, so the text is longer than its unique content justifies.

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?

There is no output schema, but the description supplies the return contract anyway: pick, its probability, every option's probability, cleared_threshold and escalate, plus the rule that a top-level escalate wins if any answer escalates and that escalated cases must go to a human with the probabilities. Combined with threshold guidance and the timeout/billing limits, an agent has everything needed to call this correctly on the first try for a seven-parameter nested tool.

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 already 100%, yet the description still adds meaning the schema cannot: put the policy in the state alongside the case and include only what the decision needs; phrase a "noul" so yes is the thing to act on; add an "I don't know" label so a choice question can abstain; order score levels lowest first; and the crucial operational note that hosted models report an uncertain yes/no at 0.80+, so a yes/no threshold must exceed 0.80. That last point in particular prevents a silently broken integration.

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 opening sentence gives a specific verb and resource (ask Myles to make a decision and return calibrated probabilities) and immediately delimits it from what an agent might assume: "Myles is not a chat model: it does not write text or follow instructions in the state." It also distinguishes it from its siblings (health, list_models) implicitly by describing a decision function rather than a lookup or status check.

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?

Provides an explicit trigger condition — "whenever you would otherwise judge something yourself and then act on it" — followed by a concrete enumeration of the exact decisions that fit (routing, fraud/scam/prompt-injection flags, approve/reject/escalate, tool-call or claim checks, scoring). It also states the negative case (not for text generation) and gives condensing advice (several questions in one call rather than one per call), so the agent knows both the fit and the operating pattern.

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

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

Annotations already cover read-only, idempotent, non-destructive, and open-world traits. The description adds a genuinely new behavioral fact not in annotations: 'Not billed,' which is valuable context for an agent deciding whether to poll this endpoint. It stops short of detailing response shape or rate limits.

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 short sentences with zero waste; the core action leads and the timing/billing context follows. Every sentence earns its place.

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-parameter tool with no output schema and rich annotations, the description covers what it checks, when to call it, and a key cost trait. It leaves the exact return format implicit, keeping it just short of fully complete.

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 and schema coverage is 100%, so there is nothing for the description to compensate for. Baseline 4 applies.

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 (check) and resource (Decision API), and adds that it reports which models have a healthy server, which distinguishes it from list_models and decide. It does not explicitly name those siblings as alternatives, so it falls short of a 5.

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?

Gives clear triggering conditions: 'before a batch of work or after an error.' This tells the agent when to reach for it, though it does not explicitly exclude or compare against list_models/decide.

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

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

The annotations already cover read-only, idempotent, non-destructive, and open-world behavior. The description adds genuinely useful context beyond the annotations: billing is not incurred, deprecated builds name their successor, and moving vs dated ids have different stability semantics.

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 front-loaded with what the tool does and then adds compact, useful details about id behavior, deprecation, and billing. Every sentence contributes information without padding.

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-parameter read-only listing tool with rich annotations, the description is nearly complete: it explains what is listed, how model ids behave, and that listing is not billed. It does not describe the exact response structure, but no output schema is provided and the tool is simple enough that this is a minor gap.

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?

This tool takes zero parameters, so the baseline is 4. The schema is empty and requires no further parameter explanation from the description.

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 and resource: it lists the decision models served by this endpoint, and it clarifies what the returned ids mean. It is clear on its own but does not explicitly differentiate itself from the sibling tools health and decide.

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 implies usage by explaining moving versus dated model ids for selection and repeatability, but it gives no explicit guidance on when to call this tool instead of health or decide. The reader can infer the purpose, but tool-selection guidance is absent.

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