list_dags

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

Without annotations, the description carries the full burden. It discloses pagination and filtering via parameters but does not mention read-only nature, rate limits, authentication needs, or behavior with large datasets. It is adequate but not thorough.

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 uses a clear docstring format with Args and Returns sections. It is reasonably concise, though the prefix '[Tool Role]:' is unnecessary and slightly verbose. Overall, it is well-structured and easy to parse.

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 an output schema present, the description does not need to detail return values, but it does describe return structure (dict with dags list, pagination info, total counts). Missing context includes ordering, whether it returns only active DAGs, and edge cases like empty results. It is adequate but leaves gaps.

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 compensates fully by explaining all five parameters (limit, offset, fetch_all, id_contains, name_contains) with their purpose and defaults. This adds significant meaning beyond the 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 clearly states the tool lists all DAGs in the Airflow cluster with pagination support. The verb 'lists' and resource 'DAGs' are specific, and the scope 'registered in the Airflow cluster' is explicit. It effectively distinguishes from sibling tools like get_dag or running_dags.

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 no explicit guidance on when to use this tool versus alternatives, nor does it mention when not to use it. It only describes what it does, leaving the AI agent to infer usage context from sibling tool names.

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