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

No annotations are supplied, so the description carries the full burden. It discloses the return contents, which is valuable given there is no output schema, but it never states that this is a safe read-only call, whether the dataset is fixed or can be mutated, or anything about cost/pagination.

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, with the payload of what is returned stated up front and the usage cue ('call this first') placed at the end where it belongs. Slightly list-like in the first sentence but no wasted prose.

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 explain the return shape, and it does enumerate all four returned elements. It is adequate for a trivial zero-argument lookup; only the read-only/safety framing is missing.

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?

The tool takes zero parameters, so per the rubric the baseline is 4. There is nothing for the description to disambiguate, and it adds no misleading parameter guidance.

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?

Names the specific resource (PropFirmPicker dataset) and enumerates exactly what is returned: columns, numeric flags, row count, provenance banner. It is distinguishable from dataset_row / dataset_search / dataset_stats by being the schema-lookup call. It stops short of a 5 because the signposting is content-listing rather than a crisp verb+resource framing.

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?

'Call this first to learn the schema' gives explicit ordering guidance, which is genuine when-to-use direction. It does not, however, exclude or contrast with siblings that overlap, notably dataset_provenance, whose subject matter (the provenance banner) is also claimed here.

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