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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is lower. The description adds genuinely new behavioral context: cost ('Free') and an auth requirement ('private projects of your operator need your key'), which annotations do not convey. It stops short of rate limits or output-format detail, but the added auth/cost context is the valuable part.

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?

A single tightly packed sentence with the scoping phrase front-loaded ('One dataset project in detail:') followed by the content list, then the contribute_records caveat and the cost/auth note. Every clause carries information; nothing is padded.

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 full burden of explaining returns and does so by enumerating readme, schema, versions, contributors, and the conditional AI summary. Auth and cost are covered. Only minor gaps remain (no pagination/truncation hints, no slug guidance).

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 0% and the single 'slug' parameter is only implicitly referenced ('One dataset project'). The description adds no format, origin, or example for the slug even though the schema carries a regex pattern but no human-readable explanation, so it fails to compensate for the coverage 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 names a specific verb+resource ('One dataset project in detail') and enumerates the returned contents: readme, record schema, latest versions, top contributors, AI summary. This makes it clearly a metadata/schema lookup rather than a record query, so it reads differently from query_dataset/read_dataset, though it never explicitly distinguishes itself from dataset_manifest or read_dataset by name.

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 concrete workflow guidance: 'Read the schema before contribute_records — records that do not match are rejected,' which explicitly ties this tool to a sibling and states the consequence of skipping it. It also flags cost/auth ('Free; private projects of your operator need your key'), but offers no explicit when-not or alternative for the plain metadata-lookup case.

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