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

Annotations already cover read-only, idempotent, non-destructive behavior, so the bar is lower, and the description still adds meaningful context: results are anonymized aggregates ('Counted without anyone's job details') and are advisory rather than decision-grade. It does not discuss staleness, sample size, or pagination, which would have pushed this higher.

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?

Purpose and payload are front-loaded in the opening clause, and the closing caveat is short and earns its place. The first sentence is long and comma-heavy, which slightly hurts scanability but wastes no words.

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 carries the burden of describing return values and does so explicitly (pass rates, winners, cost per task, common failure), plus it explains the empty-argument case. What is missing is any indication of how much data or how recent the underlying comparisons are.

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% for the single optional parameter, so the baseline is 3. The description restates the no-argument behavior (get the list of valid job_type values) that the schema already documents, adding no new syntax or format detail.

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 resource (aggregated field comparison results on Model Source) and enumerates the payload (passes, win rates, cost per task, common failure), so an agent knows exactly what it returns. It does not, however, distinguish itself from siblings like benchmark or rank, leaving that differentiation to inference.

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?

It gives real usage context: call with no job_type to get the available list, treat the output as a starting point, and validate on the person's own work before choosing. That is implied guidance rather than explicit when-to-use/when-not rules, and no sibling alternative is named for cases where this data is insufficient.

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