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

With no annotations, the description carries the full burden. It transparently lists what the tool returns and implies a read-only schema inspection, but it does not explicitly state whether there are side effects, how the output is structured, or whether there are performance/cost implications. No contradiction, but deeper behavioral disclosure is missing.

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 description is two short sentences with no filler. The concrete output fields are front-loaded, and the usage directive 'Call this first' is appended after the substance. Every sentence contributes value.

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 zero-parameter metadata tool, the description is nearly complete: it names the dataset, the returned fields, and the intended first-use position. It could add a note about whether the schema is static or dynamic, and the sibling overlap is not disambiguated, but the core need for selecting and invoking the tool is satisfied.

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 has zero parameters and an empty input schema, so there is no parameter ambiguity to resolve. The baseline of 4 applies because no parameter documentation is needed.

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 the specific dataset (RentRollDesk) and enumerates the exact returned information: column names, numeric flags, row count, and provenance banner. It stops short of explicitly contrasting itself with dataset_stats and dataset_provenance, which may overlap on row count and provenance, so it does not fully distinguish from siblings.

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 clear usage context with 'Call this first to learn the schema,' which implies this is the entry-point tool for understanding the dataset. However, it does not state when to choose a sibling tool instead or mention any conditions under which this tool should not be used.

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