df_generate_sql

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

Annotations already mark this as readOnly, idempotent, and non-destructive, so the description adds value by disclosing the error-handling contract: failures return succeeded=false with validation_errors rather than throwing an error. This is beyond what the annotations provide and is useful for an agent planning calls. 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?

The description is two sentences, with the primary purpose front-loaded and the secondary behavioral note concise. Every word earns its place; there is no redundancy or fluff.

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?

The description covers the tool's core behavior and error handling but omits the success return value (presumably the SQL text) and any parameter semantics. With six parameters and no output schema, an agent needs more detail to call this correctly, though the safety annotations mitigate some risk.

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 description adds zero information about any of the six parameters. Schema coverage is only 50% (descriptions for offset, project_id, version_id), leaving data_mart_id, limit, and language unexplained in both schema and description. The description should compensate for this gap but does not.

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 states a specific verb ('Generate'), a precise resource ('the SQL query of a data mart'), and immediately clarifies that nothing is executed or stored. This distinguishes it from sibling tools that fetch or write data mart definitions, such as df_get_data_mart or df_get_data_mart_view, without needing to inspect their schemas.

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 implies this tool is for generating SQL without side effects, but it never explicitly states when to use it instead of alternatives. It does not mention any conditions, exclusions, or comparable tools, leaving the agent to infer the intended use case from the behavior note.

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