Ops MCP Server

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it returns formatted output with task states, durations, and try counts, includes hints for failed tasks, and provides a common DAG task flow for context. It also warns about the 'env' parameter requirement. However, it doesn't mention potential errors, rate limits, or authentication needs.

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 appropriately sized. It starts with the core purpose, provides usage guidance, includes a practical example flow, details parameters clearly, and ends with return information. Every sentence adds value without redundancy, making it easy to parse and understand quickly.

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 (DAG run details with task instances), no annotations, and an output schema that likely handles return values, the description is complete. It covers purpose, usage, parameters, behavioral output, and integration hints (like get_task_log). The presence of an output schema means the description doesn't need to detail return structure, allowing it to focus on contextual guidance.

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 description adds significant meaning beyond the input schema, which has 0% description coverage. It explains 'dag_id' as 'The DAG identifier', 'dag_run_id' with format examples, and 'env' with allowed values and a critical warning about not guessing/defaulting. This fully compensates for the schema's lack of descriptions and provides essential context for proper usage.

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 with specific verbs ('Get full details', 'see ALL tasks') and resources ('specific DAG run', 'every task instance'). It distinguishes from sibling tools like 'list_dag_runs' (which lists runs) and 'get_task_log' (which gets logs for specific tasks) by focusing on comprehensive run details with task statuses.

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 guidance on when to use this tool ('Use this to see ALL tasks in a DAG run with their pass/fail status') and includes practical context ('For any failed tasks, the output includes ready-to-use get_task_log() hints'). It also distinguishes from alternatives by emphasizing comprehensive details rather than just listing runs or getting specific logs.

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