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

With no annotations, the description carries the full burden and does well: it discloses the return shape (text plus structured list), the price unit ($ per 1M tokens), and the non-obvious @low/@medium/@high reasoning-effort modifier on model ids. It omits auth/rate-limit/pagination behavior, so not a 5.

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-loaded with what is returned before the downstream instruction. Every clause carries information the agent needs.

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?

No output schema exists, and the description compensates by describing the return format and the reasoning-effort suffix syntax. Filters are covered by the schema. Minor gaps: nothing about default ordering or result size, but nothing an agent needs to call it correctly is missing.

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 description coverage is 100%, so the schema already documents search, images_only, and max_output_price. The description never references these filters, but the price-per-1M-tokens framing gives partial context for max_output_price. Baseline 3 applies when the schema does the heavy lifting.

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 and resource ('Current OpenMark model ids') plus the two things it returns (text and structured list) and the price unit. No sibling (get_account, estimate_cost, get_results) does model enumeration, so the agent can distinguish it 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 these ids in run_benchmark,' which tells the agent when this tool is the right first step. It stops short of naming when-not or alternatives (e.g. estimate_cost for pricing math), but the workflow context is clear.

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