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

Annotations already fully declare the safety profile (readOnly, idempotent, non-destructive, closed-world), so the description need not restate that. It adds that derived_counts are computed from the relations, which is useful behavioral context, but says nothing about the shape or size of the returned metadata block.

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 compact sentences with the content list front-loaded, so the agent knows what it gets immediately. The second sentence is slightly tangential and introduces derived_counts without clarifying its status, which costs a point.

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 with no output schema, listing the returned content (version, snapshot date, counts, legend, citation) is close to sufficient. The unresolved ambiguity about whether derived_counts is a field or a separate operation is the main gap.

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. Schema coverage is 100% and there is nothing further the description could meaningfully add about 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 enumerates exactly what the tool returns: dataset version, corpus snapshot date, counts, evidence-code legend, and citation guidance. That is specific and distinguishable from the entity-lookup siblings. It lacks an explicit verb, and the derived_counts sentence blurs whether that is a returned field or another tool.

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?

Usage is implied rather than stated: an agent must infer this is the entry point for dataset provenance and citation info. The one concrete pointer is the tie to find_contradictions for comparing derived contested/conflicting counts, but there is no explicit when-to-use or when-not-to-use framing.

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