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

The annotations already signal read-only safety, and the description adds valuable detail: it lists the return fields, explains the default dataset-wide behavior when no indicator is given, and warns about the per-district `estimated` flag. This goes well beyond the structured annotations.

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 three sentences, front-loaded with the most important usage rule, followed by a compact list of outputs and a crucial caveat. Every sentence earns its place with no fluff.

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 read-only tool with one optional parameter and no output schema, the description covers what it returns, when to call it, and the critical `estimated` flag nuance. An agent has enough context 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 input schema already fully documents the single `indicator` parameter, including the omit-for-dataset-wide behavior. The description reinforces this but does not add meaning beyond the schema, so the baseline of 3 applies.

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 states a clear verb-resource pair: retrieving methodology and citation data. It specifies exactly what the tool returns (source, vintage, definition, caveat, estimation method, licence attribution, citation string) and differentiates the one-indicator vs dataset-wide scopes, making it distinct from the sibling data-access 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 gives explicit trigger conditions: use whenever a number will be stated, or when asked about how a figure was produced, its age, or trustworthiness. It does not name specific sibling alternatives or exclusions, but the usage context is strong enough that an agent can decide when to invoke this tool.

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