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

Annotations already declare read-only, idempotent, non-destructive, closed-world behavior, so safety is covered. The description adds real value by enumerating the returned data (rates, thresholds, fallback targets, pricing_snapshot_id, Merkle root), which tells the agent what this fixed-size snapshot contains.

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?

One sentence with the action and resource front-loaded, followed by the returned-field enumeration. It is dense but every clause carries information; no filler or repetition.

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, and the description compensates by listing the exact fields returned, plus the fixed count of 5 models. Combined with annotations covering the safety profile, an agent has everything needed to call and interpret this 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?

The tool takes no parameters, so parameter semantics are moot and the baseline of 4 applies. The description correctly avoids inventing inputs and focuses on output content.

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 concrete verb and resource ('Return the 5 deterministic cliff-engine models supported by check_context_cliff') and names the sibling tool it is tied to. Scope is explicit, though it doesn't distinguish itself from etuki_search_models, which also returns models.

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 — call this to discover which models are available before invoking check_context_cliff — but the description never says when to prefer this over the sibling etuki_search_models or etuki_get_audit_evidence. No exclusions or prerequisites are stated.

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