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

The annotations already declare readOnlyHint, idempotentHint, openWorldHint=false and destructiveHint=false, so the safety profile is fully covered without the description. The description adds useful workflow context (it is a precursor to semantic_query) and domain scoping ('verified emissions data'), but says nothing about what the call actually returns, which matters because there is no output schema.

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?

A single front-loaded sentence that opens with the trigger condition and closes with the next step. No redundant restatement of the tool name or filler.

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-argument, read-only discovery tool with full annotation coverage, the agent needs only to know why and when to call it, which the description supplies. The one gap is the absence of any hint about the return shape (a list of question patterns?), which is not covered by an output schema.

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 there is nothing for the description to disambiguate and no risk of misuse at call time. Baseline 4 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 concrete outcome — seeing which kinds of questions the verified emissions data can answer — which tells an agent this is a discovery/enumeration tool rather than a query tool. It is distinguishable from the sibling semantic_query, though it never uses the word 'templates' or says explicitly that it returns a list of question patterns (that information lives only in the title).

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 explicit sequencing guidance ('before calling semantic_query') and names the alternative tool the agent should use next. It stops short of saying when not to use it, but for a zero-cost read-only discovery call the context is clear enough.

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