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

With no annotations, the description carries the full disclosure burden and does add real value: it details what the response contains (boards with condition key and label, recent papers, introducing paper, per-year distribution) and discloses the cost ("Free · 4 credits") plus the protocol-matching caveat. It stops short of covering failure behavior for an unknown or ambiguous dataset_key, output size, or whether results are cached, which keeps it at adequate rather than strong.

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 dense and front-loaded, leading with the resource and then the payload contents before the protocol caveat and cost tag. The middle sentence is long and comma-chained, but no sentence is redundant.

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 and no annotations, the description must describe the return value, and it does so thoroughly by enumerating each section of the response. Gaps remain around how large the response is and what happens for an unrecognized dataset_key, but for a one-parameter read tool it is largely complete.

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?

There is a single required parameter with 100% schema description coverage, so the schema already explains dataset_key with the "e.g. scannet" example. The description adds only the framing that the key scopes the leaderboard to one dataset" and no format, alias-resolution, or error semantics beyond the schema, so 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?

The description gives a specific verb+resource ("Leaderboards for a dataset") and then enumerates the contents: metric boards with top methods and their papers, recent papers, the introducing paper, and per-year counts. That makes the tool's purpose unmistakable, but it never names or distinguishes itself from plausible siblings like benchmark_timeline or compare_on_benchmark.

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?

Usage is only implied: an agent can infer this is the tool for a dataset-wide ranking overview, and the note that "rankings compare matching protocols only" hints at when this view is appropriate. There is no explicit when-to-use, when-not-to-use, or named alternative, so the agent must infer selection from the name and content list.

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