astro-airflow-mcp

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

With no annotations provided, the description carries the full burden. It effectively describes the tool's behavior: it's a read-only operation (implied by 'Get'), returns complete DAG information, and specifies the output format as JSON. However, it doesn't mention potential errors (e.g., if DAG doesn't exist) or performance aspects, leaving minor gaps.

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 and front-loaded with the core purpose, followed by usage examples, return details, and parameter info. Every sentence adds value without redundancy, making it efficient and easy to parse for an AI agent.

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 (single parameter, no annotations, but with an output schema), the description is complete. It covers purpose, usage, parameter semantics, and detailed return values, compensating for the lack of annotations and low schema coverage. The output schema existence means return format details are less critical.

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 schema description coverage is 0%, but the description compensates by clearly explaining the single parameter 'dag_id' as 'The ID of the DAG to get details for' in the Args section. This adds essential meaning beyond the bare schema, though it could elaborate on format constraints (e.g., string patterns).

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 explicitly states 'Get detailed information about a specific Apache Airflow DAG' with a clear verb ('Get') and resource ('DAG'), and distinguishes it from siblings like 'list_dags' (which lists multiple DAGs) and 'get_dag_run' (which focuses on runs rather than DAG metadata). The specific examples further clarify the scope.

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 explicit usage scenarios with bullet points covering when to use this tool (e.g., for details, schedule, pause status, ownership, tags) and implicitly distinguishes it from alternatives like 'list_dags' (for listing) or 'get_dag_run' (for run-specific data). The context is clear and comprehensive.

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