list_dags

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

No annotations are provided, so the description must carry behavioral transparency. It discloses the return structure (dict with dags list, pagination info, total counts) and explains fetch_all behavior. It doesn't discuss permission requirements or performance impacts, but for a read-only listing tool this is reasonably transparent.

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 concise and well-structured: a one-line summary followed by clear Args and Returns sections. Every sentence provides necessary information with 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?

Given the tool has 5 parameters, an output schema, and no nested objects, the description is complete. It covers purpose, parameters, pagination/filtering options, and return payload composition. No additional context seems needed for correct invocation.

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%, meaning the input schema only shows types and defaults. The description compensates fully by explaining each parameter's meaning: limit (max DAGs), offset (skip), fetch_all (bypass pagination), id_contains (filter by ID), and name_contains (filter by display name). This is valuable semantic content 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's purpose: 'Lists all DAGs registered in the Airflow cluster with pagination support.' This distinguishes it from siblings like get_dag (single DAG), get_dags_detailed_batch (batch detail), running_dags, and failed_dags by specifying the full list scope and pagination.

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 clear context: use this to list all DAGs with optional filters and pagination. It does not explicitly mention alternatives or exclusions, but the scope is defined well enough to infer when this tool is appropriate compared to more specific sibling tools.

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