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MCP-Airflow-API

MCP-Airflow-API

by call518
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

dag_code

Retrieve the source code of a specified Apache Airflow DAG by providing its exact DAG ID for direct inspection without API navigation.

Instructions

[Tool Role]: Retrieves the source code for the specified DAG.

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_code"
  }
}

Input Schema

NameRequiredDescriptionDefault
dag_idYesExact DAG identifier, as returned by list_dags; not its display name.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv3.7.4
    • addedInput schema / properties / dag_id / description
      Added value: +"Exact DAG identifier, as returned by list_dags; not its\ndisplay name."
  2. Addedv3.7.2
  3. Removedv3.6.5
  4. Changed2 schema fields changedv3.6.6
    • addedInput schema / additionalProperties
      Added value: +false
    • removedInput schema / properties / dag_id / title
      Removed value: -"Dag Id"
  5. First observed

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full disclosure burden, yet it says nothing about read-only nature, permission requirements, size limits, or truncation for large DAG files. Only the bare retrieval action is stated.

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

Conciseness4/5

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

A single front-loaded sentence with no filler. The only minor overhead is the '[Tool Role]:' prefix, which is boilerplate rather than information.

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

Completeness3/5

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

An output schema exists, so return format need not be explained, and the single parameter is fully documented. However, with zero annotations and no behavioral notes, the definition is adequate but thin for a tool an agent may call on arbitrarily large DAGs.

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

Parameters3/5

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

Schema coverage is 100% and the schema already explains that dag_id is the exact identifier as returned by list_dags, not the display name. The description adds no further semantics beyond the schema, so the baseline 3 applies.

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

Purpose4/5

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

Uses a specific verb ('Retrieves') and resource ('source code for the specified DAG'), so the agent knows exactly what comes back. It does not differentiate from near-neighbors like get_dag or dag_graph, which likely return metadata/structure rather than source, leaving a small ambiguity.

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 on when to pick this over get_dag or dag_graph, and no prerequisites or exclusions stated. The agent must infer from the name alone that this returns raw source text rather than parsed structure.

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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