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MCP-Airflow-API

MCP-Airflow-API

by fastmcp-me
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

dag_task_duration

Analyze task durations in an Airflow DAG run to identify slow-running tasks, spot bottlenecks, and optimize workflow performance.

Instructions

[Tool Role]: Analyzes task durations within a DAG run.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "dag_id": "<dag_id>"
    },
    "name": "dag_task_duration"
  }
}

Input Schema

NameRequiredDescriptionDefault
dag_idYes
dag_run_idNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.0.0

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It implies a read-only analysis but does not explicitly state whether it is non-mutating, require permissions, or describe the output. It also does not mention how dag_run_id affects behavior, leaving significant gaps in transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, no-waste sentence, which is concise. However, it is overly terse and omits critical details, making it under-specified rather than efficiently concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having an output schema, the description does not explain what the output contains or how to interpret it. It also lacks context on the significance of task durations and when to use this over other DAG analysis tools. The close sibling 'dag_run_duration' amplifies the need for more completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% coverage, so the description must compensate. It mentions 'within a DAG run' but does not explain the roles of dag_id or dag_run_id, nor the effect of leaving dag_run_id null. The names are self-descriptive, but the description adds minimal value beyond them.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb 'Analyzes' and clearly identifies the resource as 'task durations within a DAG run.' This distinguishes it from the sibling tool 'dag_run_duration', which presumably analyzes DAG run durations as a whole, providing explicit differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. Given the similar sibling 'dag_run_duration', the description fails to mention any exclusions or preferred contexts, leaving the agent without usage direction.

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

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