Get one AI benchmark with its SOTA and model scores

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

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so the safety profile is covered. The description adds real behavioral context beyond that: the breadth of the returned record and a citation convention ('Cite `canonical_url` with `dataset_version`'), which is not derivable from any structured field. It does not mention behavior on an unknown slug.

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, no preamble: the returned content is front-loaded and the workflow/citation guidance follows. The middle enumeration is long but each item is a distinct field an agent would otherwise have to discover, so it earns its space.

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?

An output schema exists, so return values need no explanation—the description's field list is over-delivery rather than a necessity. Combined with the routing guidance and citation convention, an agent has enough to select and call this correctly; only failure behavior on a bad slug is unaddressed.

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% and the single parameter is already documented with examples ('swe-bench-pro') and the slug-or-name rule. The description's 'by slug or name' only restates the schema, so the baseline 3 applies with no added semantics.

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 ('Get one benchmark by slug or name') and then enumerates exactly what comes back: what it measures, SOTA, saturation, headroom, human baseline, source/notes, radar axis, model scores, profession rankings. This is clearly distinguishable from the sibling list_benchmarks, which only enumerates.

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 situates the tool in a workflow: 'Use this after `list_benchmarks` or `search_all`, or to check what a ranking rests on.' It names the alternatives that produce the required slug, but does not state a when-not condition (e.g., that bulk enumeration belongs to list_benchmarks).

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