# Agent Guidelines for astro-airflow-mcp

> Guidelines for AI coding assistants contributing to this repository.

- Skill: `tools-only/agent-guidelines-for-astro-airflow-mcp` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add tools-only/agent-guidelines-for-astro-airflow-mcp`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tools-only/agent-guidelines-for-astro-airflow-mcp/raw
- Safety review: pending (external: skill-scanner PASS, skillspector CAUTION)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: tools-only (https://skillmd.com/u/tools-only)
- Updated: 2026-09-29
- Page: https://skillmd.com/skills/tools-only/agent-guidelines-for-astro-airflow-mcp

---

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**Table of Contents**  *generated with [DocToc](https://github.com/thlorenz/doctoc)*

- [Agent Guidelines for astro-airflow-mcp](#agent-guidelines-for-astro-airflow-mcp)
  - [Architecture](#architecture)
  - [Code Conventions](#code-conventions)
    - [HTTP Client](#http-client)
    - [Adapter Pattern](#adapter-pattern)
    - [MCP Tools](#mcp-tools)
    - [Error Handling](#error-handling)
  - [Airflow Version Differences](#airflow-version-differences)
  - [Testing](#testing)
  - [Files to Know](#files-to-know)

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# Agent Guidelines for astro-airflow-mcp

Guidelines for AI coding assistants contributing to this repository.

## Architecture

MCP server for Apache Airflow with adapter pattern for version compatibility:

```
src/astro_airflow_mcp/
├── server.py          # Core infrastructure: config, auth, adapter management (FastMCP)
├── tools/             # MCP tool implementations (grouped by domain)
│   ├── dag.py         # DAG management (get, list, source, stats, pause, unpause)
│   ├── task.py        # Task management (get, list, instance, logs, clear)
│   ├── dag_run.py     # DAG run management (list, get, trigger, trigger_and_wait, delete, clear)
│   ├── asset.py       # Asset/dataset tools (list, events, upstream events)
│   ├── admin.py       # Admin tools (connections, variables, pools, plugins, providers, config, version)
│   └── diagnostic.py  # Diagnostic tools (warnings, errors, explore, diagnose, health)
├── resources.py       # MCP resources (read-only endpoints)
├── prompts.py         # MCP prompts (guided workflows)
├── adapters/
│   ├── base.py        # Abstract adapter interface
│   ├── airflow_v2.py  # Airflow 2.x API (/api/v1)
│   └── airflow_v3.py  # Airflow 3.x API (/api/v2)
├── models.py          # Pydantic models (documentation/type reference)
└── plugin.py          # Airflow 3.x plugin integration
```

## Code Conventions

### HTTP Client

Use `httpx`, not `requests`. All HTTP calls should use `httpx.Client` and pass `self._verify` for SSL configuration:

```python
# Good
with httpx.Client(timeout=30.0, verify=self._verify) as client:
    response = client.get(url, headers=headers)

# Bad
response = requests.get(url, headers=headers)
```

### Adapter Pattern

All Airflow API calls go through adapters. Never call Airflow API directly from MCP tools:

```python
# Good - use adapter
adapter = _get_adapter()
data = adapter.list_dags(limit=100)

# Bad - direct API call
response = httpx.get(f"{url}/api/v2/dags")
```

When adding new API endpoints:
1. Add abstract method to `adapters/base.py`
2. Implement in both `airflow_v2.py` and `airflow_v3.py`
3. Handle API differences (field names, paths, availability)

### MCP Tools

Tools need descriptive docstrings for AI discovery:

```python
@mcp.tool()
def get_dag_details(dag_id: str) -> str:
    """Get detailed information about a specific DAG.

    Use this tool when the user asks about:
    - "Show me details for DAG X"
    - "What's the schedule for DAG Y?"

    Args:
        dag_id: The ID of the DAG

    Returns:
        JSON with DAG metadata
    """
```

### Error Handling

Adapters handle missing endpoints gracefully:

```python
try:
    return self._call("newEndpoint")
except NotFoundError:
    return self._handle_not_found(
        "newEndpoint",
        alternative="Use alternativeEndpoint instead"
    )
```

## Airflow Version Differences

| Feature | Airflow 2.x | Airflow 3.x |
|---------|-------------|-------------|
| API path | `/api/v1` | `/api/v2` |
| Auth | Basic auth | OAuth2/JWT |
| Assets | `datasets` | `assets` |
| DAG runs | `execution_date` | `logical_date` |

## Testing

- Mock adapters, not HTTP libraries
- Unit tests: `tests/`
- Integration tests: `tests/integration/` (require running Airflow)

```python
# Good - mock adapter (patch in the tool module that imports it)
mock_adapter = mocker.Mock()
mock_adapter.list_dags.return_value = {"dags": [...]}
mocker.patch("astro_airflow_mcp.tools.dag._get_adapter", return_value=mock_adapter)

# Bad - mock HTTP library
mocker.patch("httpx.Client")
```

## Files to Know

- `server.py`: All MCP tools defined here
- `adapters/base.py`: Interface contract for both versions
- `models.py`: Type reference (not used for validation currently)
- `docker-compose.test.yml`: Test against real Airflow instances