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

With no annotations provided, the description carries the full burden and does so thoroughly. It discloses that the query is never executed or checked against a database, that names are guessed unless supplied, that results are cached, that there are rate limits and a 401 refusal, that the call is paid, and that output is capped around 500 tokens. This is exemplary disclosure.

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 every sentence adds necessary operational or behavioral detail. It is front-loaded with the core purpose, then returns the contract, limitations, cautions, quotas, and caching behavior. No filler is present.

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 generation tool with no output schema and no annotations, the description is remarkably complete. It covers the return format, what is not returned, execution behavior, schema knowledge limitations, cost, rate limits, token cap, and caching, leaving little ambiguity for an agent choosing or invoking the tool.

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%, so the baseline is 3, and the description adds meaningful context beyond the schema. It explicitly notes that dialect is accepted for forward compatibility but not passed to the model, and it advises the caller to include table and column names in description because the model otherwise invents plausible ones.

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 opens with a specific verb and resource: 'Translate a natural-language request into a SQL statement.' It clearly identifies the tool's function and distinguishes it from text-focused siblings like generate_text and draft_email by naming SQL and the expected { sql } return shape.

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?

The description gives actionable guidance: include table and column names in the request, review output before running, and be especially careful with writes. It does not explicitly name alternatives or state when not to use the tool, but the context is clear enough for an agent to decide when SQL generation is appropriate.

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