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

Annotations already cover read-only, idempotent, and open-world traits. The description adds genuinely useful context beyond them: the ~300-model scope, vendor coverage, and the pricing model (provider cost × 1.5, what experiments actually bill), which the agent cannot infer from structured fields.

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?

Front-loaded with the core purpose, followed by filters, downstream routing, and a compact example. Every element earns its place, though the vendor enumeration and parenthetical pricing note add mild 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 does the work of explaining returns (model ids, context window, per-token prices) and their use. Adequate for a read-only list tool, though pagination/limit behavior beyond the default isn't discussed.

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 100%, so the baseline is 3. The description adds value by clarifying that 'max price' is billed output price and by giving a concrete usage example combining search and min_context, which aids correct invocation.

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?

Specific verb ('List') + resource ('AI models available for experiments') with concrete scope (~300 models across named vendors). The agent can distinguish this from sibling tools like experiment_create or experiment_estimate immediately.

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?

Explicitly routes the agent downstream ('Use the returned model ids in experiment_create') and enumerates the three filtering modes. It doesn't state when-not-to-use or name a competing tool, so it falls short of a 5.

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