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

There are no annotations to indicate side effects or safety, and the description does not explicitly state that the operation is read-only. However, the language 'learn the schema' strongly implies a non-mutating metadata retrieval, so the behavior is mostly transparent.

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 concise, informative, and front-loaded with the core output. Every clause contributes meaningful detail, and there is no redundant or filler language.

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 parameters, the description adequately covers the return contents: columns, numeric flags, row count, and provenance banner. The only minor gap is the unexplained 'Sopvo' name, but it does not hinder understanding.

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, so there are no parameter semantics to document. The description fully explains what the tool returns without needing parameter details.

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 clearly states the tool lists columns, identifies numeric columns, provides the row count, and shows the provenance banner. The title reinforces the schema-learning purpose, and 'Call this first to learn the schema' unambiguously distinguishes it from sibling tools.

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?

The description explicitly instructs to call this tool first to learn the schema, giving clear usage timing relative to other dataset operations. It does not explicitly name sibling alternatives, but the instruction is sufficient for a zero-parameter metadata tool.

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