Apache Airflow MCP Server

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the agent knows this is a safe, repeatable read operation. The description adds valuable behavioral context beyond annotations: it explains how sensors increment try_number, that rendered fields are truncated with metadata, and provides authoritative guidance about try_number usage for logs. No contradiction with annotations exists.

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 clear sections (Parameters, Returns, Notes) and front-loaded with the core purpose. Every sentence adds value, though the Notes section could be slightly more concise. Overall, it's appropriately sized for a complex tool with many parameters.

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 (7 parameters, 0% schema coverage), the description is remarkably complete. It explains parameters thoroughly, details the return structure, includes important behavioral notes, and references sibling tools. With annotations covering safety and an output schema presumably defining the response dict, no significant gaps remain.

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 all 7 parameters in detail. It clarifies parameter relationships (URL precedence, required identifiers unless resolved from ui_url), default values, coercion rules for max_rendered_bytes, and the purpose of include_rendered. This adds substantial meaning beyond 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 specific action ('Return task metadata, config, attempt summary, optional rendered fields, and UI URLs') and distinguishes this tool from siblings like 'airflow_get_task_instance_logs' by focusing on metadata retrieval rather than logs. It explicitly names the exact data components returned, making the 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 clear context for when to use this tool (to get task instance details) and mentions its relationship with 'airflow_get_task_instance_logs' for logs retrieval. However, it doesn't explicitly state when NOT to use it or compare it to all sibling alternatives like 'airflow_list_task_instances' for listing versus getting detailed metadata.

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