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

With no annotations, the description must disclose behavioral traits. It states the tool is for reading ('Read this') and enumerates the returned data (source, date, licence, citation), implying a safe, non-mutating operation. However, it does not explicitly confirm read-only behavior, mention any prerequisites (e.g., dataset existence), or describe error conditions. It adds some behavioral context but not a full picture.

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 short sentences, front-loaded with the core purpose (what it provides) and a clear directive for when to use it. No filler or redundancy; every word contributes to the tool's utility.

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 parameterless, read-only tool with no output schema, the description fully covers what the tool does, what data it returns, and when to use it. There is no missing information an agent would need to invoke it correctly.

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 the schema is empty. Per the rubric, a zero-parameter tool gets a baseline of 4. The description adds no parameter-specific details because there are none to document, and the schema coverage is trivially 100%. The description correctly focuses on the output rather than inputs.

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 provides provenance information (source, date, licence, citation) for the Lessonvo dataset, and positions it as the go-to for attribution. The verb 'read' plus the specific resource and purpose distinguish it from sibling tools like dataset_columns or dataset_stats, which handle different facets.

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 an explicit usage context: 'Read this to attribute a figure correctly.' This tells the agent when to use the tool. It does not enumerate alternatives or exclusions, but the sibling tools are clearly for different operations, so the usage intent is evident.

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