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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is lower. The description still adds genuine behavioral context beyond them: missing values stay unknown and insufficient observations will not declare a winner, which tells the agent how to interpret partial results.

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-load the core action and follow with a valuable caveat about result interpretation. Very little waste, though 'including coverage and timestamps' is slightly list-heavy for the space.

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?

The tool is low-complexity (two scalar params) and an output schema exists, so return values need not be described. Annotations cover safety, and the description adds interpretation guidance. The main remaining gap is parameter meaning, which drags completeness below a 5.

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?

With two required parameters and 0% schema description coverage, the description carries the full burden but does not address either. 'Saved experiment' loosely gestures at experimentId, but brandId and the UUID/format expectations are never explained, so the agent gets no semantic help differentiating the inputs.

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 clear verb (compare/retrieve) and resource (latest stored metrics for a saved experiment), so an agent knows it fetches computed metrics rather than the experiment definition. It does not explicitly distinguish itself from siblings like list_experiments or create_experiment, 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?

Usage is implied by 'for a saved experiment' – you call it to inspect a specific experiment's metrics. However, there is no explicit when-to-use, when-not, or routing to alternatives such as list_experiments, leaving the agent to infer the boundary.

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