semantic_query

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

Discloses non-obvious behavior beyond the readOnlyHint annotation: each call costs API credits and a payment-required error means a human must top up credits. This is exactly the kind of context that helps an agent act correctly. No contradiction with 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?

Three concise sentences, each carrying distinct information: the action/scope, the cost/error behavior, and query-quality guidance. Front-loaded and free of redundancy.

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 two-parameter read-only query tool with no output schema, the description covers purpose, cost, error handling, and query shaping. A mention of session continuation is missing, but the schema already documents that, so nothing critical is absent.

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?

Schema description coverage is 100%, and both parameters already have clear descriptions. The description's guidance to prefer specific questions adds marginal value for the query parameter, but it does not meaningfully extend the schema, so baseline 3 is appropriate.

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 identifies the action ('Ask a natural-language question') and the resource ('verified emissions data' across three specified knowledge graphs). It is specific about scope, but does not explicitly differentiate from sibling tools like ofp_semantics or semantic_templates, so it misses the top score.

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?

Provides useful guidance on how to phrase queries ('Prefer specific questions... over broad ones') and how to handle the payment-required error, but does not state when to use this tool versus alternatives or give any exclusion criteria. Usage is implied rather than explicit.

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