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

Annotations already cover the safety profile (readOnly, non-destructive, closed-world), so the bar is lower. The description still adds real behavioral value by disclosing the staged return: progress feed while running, full comparison with recommendation when complete. This is the kind of lifecycle detail an agent needs to decide whether to re-poll.

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 sentences, zero filler, with the core action front-loaded and the return behavior following. Every clause carries information.

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, so the description must carry the return-value burden, and it does so at a high level (two distinct response states). Combined with annotations covering safety and a fully-documented schema, an agent can call it correctly; only the concrete fields of the comparison payload are left unspecified.

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% and both parameters are documented in the schema, including the jobId provenance and the superadmin-only customerId. The description adds nothing beyond the schema on parameter meaning, so the baseline 3 applies.

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+resource: poll for benchmark job status and results. The 'benchmark' scope distinguishes it from the other status-getters (get_agent_status, get_simulation_status, get_model_optimization_status) in the sibling list. It stops short of explicitly naming a sibling, so it is clear but not maximally differentiated.

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 verb 'Poll' implies repeated invocation until completion, which is meaningful usage guidance for a status endpoint. However, it never states when to use this vs. alternatives or any prerequisite beyond the schema's 'job ID returned by benchmark_models' note. Context is implied rather than spelled out.

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