Apache Airflow MCP Server

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

The description adds valuable behavioral context beyond annotations: it specifies the return format ('Response dict: { "dag": object, "ui_url": str, "request_id": str }') and error behavior ('Raises: ToolError with compact JSON payload'). While annotations already indicate read-only, idempotent, and non-destructive operations, the description enhances understanding of output structure and error handling, which is particularly useful for an agent.

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 efficiently structured with clear sections: purpose statement, parameters explanation, return values, and error handling. Each sentence serves a distinct purpose without redundancy. The information is front-loaded with the core purpose, followed by necessary details in a logical flow.

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 (3 parameters with conditional logic) and the presence of annotations and output schema, the description provides complete context. It explains parameter interactions, specifies the return structure, and details error behavior. With annotations covering safety aspects and output schema presumably defining the response format, the description fills all necessary gaps for effective tool use.

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?

With 0% schema description coverage, the description fully compensates by explaining parameter relationships and requirements: 'Provide one; `ui_url` auto-resolves/validates the host' and 'Required when only `instance` is supplied.' This clarifies the mutual exclusivity and conditional requirements between 'instance', 'ui_url', and 'dag_id', adding essential semantic context not present in the bare 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: 'Get DAG details and a UI link.' This specifies the verb ('Get') and resources ('DAG details', 'UI link'), making it immediately understandable. However, it doesn't explicitly differentiate from siblings like 'airflow_get_dag_run' or 'airflow_list_dags', which would require more specific scope clarification.

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 for parameter usage: 'Provide one; `ui_url` auto-resolves/validates the host' and 'Required when only `instance` is supplied.' This gives practical guidance on when to use which parameters. However, it doesn't specify when to choose this tool over alternatives like 'airflow_list_dags' for listing versus getting details, or 'airflow_get_dag_run' for run-specific information.

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