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

With no annotations, the description carries the burden. It discloses that the tool returns rows with a `unit` field, warns about the rials vs. percent distinction, and emphasizes reading `unit` to avoid misreporting. This is meaningful behavioral context beyond a simple fetch, though it doesn't detail pagination or error cases.

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 dense but well-structured: it opens with the core action, lists examples, gives usage directives, and ends with a critical warning. No sentence is wasted, though the enumeration of keys could arguably be trimmed since the schema already shows examples. Overall it earns its length.

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 simple lookup tool with two params and no output schema, the description is complete. It specifies what data is available, how to use it, when to prefer it, and how to interpret the results (unit field). The mention of the `unit` field provides enough return-value 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% with descriptions for both parameters, but the description adds richer semantics by listing many valid rate_type examples (min_wage, court_fee_financial_first, cbi_price_index, lawyer_tariff_nonfinancial) and the pattern court_fee_*/lawyer_tariff_*. It clarifies the yearly nature of the data, complementing the schema without redundancy.

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 states the verb and resource: 'Fetch official Iranian yearly rates from the database' and enumerates specific rate types (min_wage, court_fee_*, etc.). It also distinguishes from siblings by explicitly redirecting دیه to calculate_diyeh, making the tool's scope unambiguous.

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 explicit when-to-use guidance: 'ALWAYS use this instead of recalling a rate — yearly figures are exactly what models misremember.' It also names an alternative for a specific case: 'For دیه use calculate_diyeh instead.' This is clear and actionable.

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