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

With no annotations provided, the description carries the disclosure burden alone. It usefully enumerates the returned fields (source, date computed, licence, citation), which tells the agent what behavior to expect from this read-only metadata call. It says nothing about permissions, caching, or freshness guarantees, but for a zero-parameter lookup of static metadata the risk surface is minimal, so a 3 is appropriate.

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, both earning their place: the first enumerates the returned content, the second states the action the agent should take. The most decision-relevant information is front-loaded.

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, the description must convey the return values, and it does so by listing the four provenance fields an agent will receive. It stops short of describing the response structure or format, but for a simple metadata lookup this is nearly complete.

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 schema declares zero parameters, so there is nothing for the description to disambiguate. The baseline for a no-parameter tool is 4, and nothing in the description contradicts the empty schema.

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 a specific resource and enumerates exactly what it returns: source, computed date, licence, and citation for the CGT HQ dataset. That is far more informative than a tautology, and the function is plainly distinct from data-oriented siblings like dataset_columns or dataset_stats, though it never explicitly contrasts itself with them.

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?

"Read this to attribute a figure correctly" gives one clear usage condition (attribution/citation), which is a genuine trigger. However, it names no alternatives and offers no exclusions, so an agent must infer when provenance is not needed. This is implied usage rather than explicit guidance.

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