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

With no annotations, the full burden falls on the description. It adds the cost warning '[x402...pay-per-use]' but otherwise only restates the operation. It does not disclose output format, potential errors, authentication requirements, or side effects.

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 short and purpose-first. The 'input=request' fragment is slightly redundant given the schema, but overall there is no unnecessary verbiage or structural clutter.

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?

With no output schema or return-value description, the agent has to infer the tool returns generated SQL. The description is adequate for selection but incomplete for fully predictable invocation, since it omits output format details and possible edge cases.

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 schema already documents the only parameter with 100% coverage as a natural-language request. The description's 'input=request' adds negligible meaning and does not provide additional SQL dialect or formatting guidance.

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: 'Generate SQL from natural language'. It is specific enough to identify the output domain, but it does not explicitly differentiate itself from siblings like code-gen or inference by naming them or providing a 'not-for' condition.

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?

'For data agents/analysts' provides useful context about who should use it, and 'input=request' tells the caller how to think about the input. It does not, however, say when not to use it or point to an alternative sibling for general code generation.

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