generate_dag_from_contract

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions validation logic, CDW connection, and default schedule, which is useful. However, it is ambiguous whether validation happens at generation time or inside the generated DAG, and it does not disclose output format or side effects beyond 'Generate Airflow DAG Python'.

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 well-structured with a clear opening, behavioral details, and an Args list. It is front-loaded and every sentence adds information, though the Args section is somewhat verbose and could be tightened.

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?

Given the tool's complexity and lack of annotations, the description covers key aspects: purpose, parameters, defaults, and validation behavior. It is nearly complete but leaves gaps around the 'no WAP' qualifier, the distinction from deploy_contract_dq_job, and the exact timing of validation. The presence of an output schema mitigates the need to describe return values.

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 description coverage is 0%, so the description must compensate. The Args section explains all four parameters with meanings and defaults (e.g., contract_json, schedule, cdw_connection_id, audit_table). This adds substantial value beyond the bare input 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 opens with 'Generate Airflow DAG Python from an ODCS data contract (no WAP)', which clearly states the tool's verb, resource, and scope. This distinguishes it from sibling tools like deploy_contract_dq_job and preview_contract_checks, making its purpose 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?

The description provides context about defaults and validation behavior but does not explicitly state when to use this tool versus alternatives like deploy_contract_dq_job or preview_contract_checks. Usage is implied by the name and purpose, but no explicit exclusions or alternative comparisons are given.

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