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

Annotations already establish readOnlyHint=true and openWorldHint=true. The description adds useful behavioral context by clarifying that results are candidates for testing rather than a recommendation, but it does not disclose ranking, limitations, or output shape.

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 tightly written sentences, with the core purpose front-loaded and the important caveat immediately following. There is no filler.

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 seven-parameter retrieval tool with no output schema, the description gives only a high-level indication of what is returned. It is sufficient to understand the tool's intent, but it does not describe the candidate result structure or any workload assumptions beyond the schema defaults.

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 all seven parameters are already documented, including defaults and the min_capability behavior. The description reinforces the capability threshold and workload basis but adds no parameter-specific semantics beyond the schema.

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 task: finding cheaper models that meet or exceed the given model's capability, costed on the same workload. It also clarifies the return is test candidates, not a recommendation, which distinguishes it from the price/cost siblings.

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 intended use is implied by "cheaper models that score at or above the given one," but there is no explicit when-to-use or when-not-to-use guidance relative to get_price or estimate_cost. The caveat "Returns candidates to test, not a recommendation" helps interpretation but not tool selection.

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