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

Annotations already establish a safe read-only, idempotent operation. The description adds useful context about net amounts, VAT rate inclusion, and URE decision/PDF references, but it does not describe caching, rate limits, or output format beyond what the output schema likely covers.

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 a single dense sentence packed with domain-specific jargon and semicolons. It is front-loaded and free of fluff, but the structure is run-on and could be more scannable for an agent.

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 tool with three optional parameters, full schema coverage, annotations, and an output schema, the description is largely complete. It could say more about default behavior and result ordering, but it provides sufficient domain context for correct invocation.

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 coverage is 100%, and each parameter already has a clear description in the schema. The description mentions operators and tariff areas, which loosely relates to the 'area' parameter, but it does not add syntax, filtering behavior, or format details beyond the 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 clearly specifies the domain and exact data returned: household electricity and gas tariffs approved by URE, including tariff groups and rate types. It effectively distinguishes this from generic datasets, though it does not explicitly differentiate itself from a sibling like get_fuel_prices.

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?

No explicit when-to-use guidance or alternatives are provided. The phrase 'in force today' implies a use case for current tariffs, but there is no instruction on when to prefer this over siblings such as get_fuel_prices or get_dataset.

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