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

With no annotations, the description carries the burden and discloses substantial behavior: ordered record-setting papers, delta over previous record, holding duration, all dated entities as points, and the believable=false flag for out-of-band jumps. It also states credit cost. It does not cover pagination, result limits, or error conditions.

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?

The definition is front-loaded with the core purpose and output shape, then use cases, condition guidance, and the believability caveat. It is dense but every sentence adds relevant information; the pricing tag is useful though slightly appended.

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 three-parameter query tool with no output schema and no annotations, the description explains the return structure well enough to call it correctly. Minor gaps remain around exact dataset/metric syntax and result limits, but the core semantics are covered.

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 only 33%, so the description must compensate. It clarifies that dataset and metric define the SOTA progression and that condition is an exact key from dataset_leaderboard. However, it leaves the accepted formats for dataset and metric implicit, so it only partially fills the schema gap.

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 precisely what the tool returns: SOTA progression on one dataset×metric over time, with record-setting papers, values, deltas, and holding durations. It also distinguishes the optional condition scope and the believability flag. An agent can tell this apart from a simple leaderboard lookup.

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 explicit use cases ('how has X improved on Y', 'when did the record last change', plotting a benchmark) and a prerequisite for condition selection from dataset_leaderboard. It does not name when not to use it or explicitly contrast it with compare_on_benchmark, so it stops short of full alternative guidance.

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