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get_config_section

Retrieve all configuration options from a specific section in Apache Airflow, enabling users to inspect and manage cluster settings without API complexity.

Instructions

[Tool Role]: Gets all options within a specific configuration section.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
section_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The core handler function for the 'get_config_section' tool. It retrieves a specific section from Airflow's configuration by making an API request to /config and extracting the requested section.
    @mcp.tool()
    async def get_config_section(section_name: str) -> Dict[str, Any]:
        """[Tool Role]: Gets all options within a specific configuration section."""
        try:
            resp = await airflow_request("GET", "/config")
            resp.raise_for_status()
            config_data = resp.json()
            
            section_data = config_data.get("sections", {}).get(section_name)
            if not section_data:
                return {"error": f"Section '{section_name}' not found"}
            
            return {
                "section_name": section_name,
                "options": section_data.get("options", {}),
                "options_count": len(section_data.get("options", {}))
            }
        except Exception as e:
            return {
                "error": f"Configuration access denied: {str(e)}",
                "note": "This requires 'expose_config = True' in airflow.cfg [webserver] section"
            }
  • Registration entry point for v1 API tools. Sets the v1-specific airflow_request function and calls register_common_tools(mcp), which registers the get_config_section tool among others.
    def register_tools(mcp):
        """Register v1 tools by importing common tools with v1 request function."""
        
        logger.info("Initializing MCP server for Airflow API v1")
        logger.info("Loading Airflow API v1 tools (Airflow 2.x)")
        
        # Set the global request function to v1
        common_tools.airflow_request = airflow_request_v1
        
        # Register all 56 common tools (includes management tools)
        common_tools.register_common_tools(mcp)
        
        # V1 has no exclusive tools - all tools are shared with v2
        
        logger.info("Registered all Airflow API v1 tools (56 tools: 43 core + 13 management tools)")
  • Registration entry point for v2 API tools. Sets the v2-specific airflow_request function and calls register_common_tools(mcp), which registers the get_config_section tool among others.
    def register_tools(mcp):
        """Register v2 tools: common tools + v2-exclusive asset tools."""
        
        logger.info("Initializing MCP server for Airflow API v2")
        logger.info("Loading Airflow API v2 tools (Airflow 3.0+)")
        
        # Set the global request function to v2
        common_tools.airflow_request = airflow_request_v2
        
        # Register all 43 common tools
        common_tools.register_common_tools(mcp)
        
        # Add V2-exclusive tools (2 tools)
        @mcp.tool()
        async def list_assets(limit: int = 20, offset: int = 0,
                             uri_pattern: Optional[str] = None) -> Dict[str, Any]:
            """
            [V2 New] List all assets in the system for data-aware scheduling.
            
            Assets are a key feature in Airflow 3.0 for data-aware scheduling.
            They enable workflows to be triggered by data changes rather than time schedules.
            
            Args:
                limit: Maximum number of assets to return (default: 20)
                offset: Number of assets to skip for pagination (default: 0)
                uri_pattern: Filter assets by URI pattern (optional)
                
            Returns:
                Dict containing assets list, pagination info, and metadata
            """
            params = {'limit': limit, 'offset': offset}
            if uri_pattern:
                params['uri_pattern'] = uri_pattern
                
            query_string = "&".join([f"{k}={v}" for k, v in params.items()])
            
            resp = await airflow_request_v2("GET", f"/assets?{query_string}")
            resp.raise_for_status()
            data = resp.json()
            
            return {
                "assets": data.get("assets", []),
                "total_entries": data.get("total_entries", 0),
                "limit": limit,
                "offset": offset,
                "api_version": "v2",
                "feature": "assets"
            }
    
        @mcp.tool()
        async def list_asset_events(limit: int = 20, offset: int = 0,
                                   asset_uri: Optional[str] = None,
                                   source_dag_id: Optional[str] = None) -> Dict[str, Any]:
            """
            [V2 New] List asset events for data lineage tracking.
            
            Asset events track when assets are created or updated by DAGs.
            This enables data lineage tracking and data-aware scheduling in Airflow 3.0.
            
            Args:
                limit: Maximum number of events to return (default: 20)
                offset: Number of events to skip for pagination (default: 0)
                asset_uri: Filter events by specific asset URI (optional)
                source_dag_id: Filter events by source DAG that produced the event (optional)
                
            Returns:
                Dict containing asset events list, pagination info, and metadata
            """
            params = {'limit': limit, 'offset': offset}
            if asset_uri:
                params['asset_uri'] = asset_uri
            if source_dag_id:
                params['source_dag_id'] = source_dag_id
                
            query_string = "&".join([f"{k}={v}" for k, v in params.items()])
            
            resp = await airflow_request_v2("GET", f"/assets/events?{query_string}")
            resp.raise_for_status()
            data = resp.json()
            
            return {
                "asset_events": data.get("asset_events", []),
                "total_entries": data.get("total_entries", 0),
                "limit": limit,
                "offset": offset,
                "api_version": "v2",
                "feature": "asset_events"
            }
    
        logger.info("Registered all Airflow API v2 tools (43 common + 2 assets + 4 management = 49 tools)")
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 burden of behavioral disclosure. It states it 'Gets' options, implying a read-only operation, but doesn't specify permissions, rate limits, error handling, or output format. For a tool with zero annotation coverage, this is insufficient to inform safe and effective use.

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

Conciseness5/5

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

The description is a single, efficient sentence with no wasted words, clearly front-loaded with the tool's role. It's appropriately sized for a simple retrieval tool, making it easy for an agent to parse quickly.

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?

Given the tool has an output schema (which handles return values) and low complexity, the description is minimally adequate. However, with no annotations and incomplete parameter guidance, it leaves gaps in behavioral and usage context, making it just viable but not comprehensive.

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?

The input schema has 1 parameter with 0% description coverage, but the description adds meaning by clarifying that 'section_name' refers to a 'specific configuration section'. This compensates partially, though it doesn't detail format or constraints. With low schema coverage, the description provides some value but not full compensation.

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?

The description clearly states the verb ('Gets') and resource ('all options within a specific configuration section'), making the purpose evident. However, it doesn't explicitly differentiate from sibling tools like 'get_config' or 'search_config_options', which likely have overlapping functionality, preventing a perfect score.

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?

The description provides no guidance on when to use this tool versus alternatives such as 'get_config' or 'search_config_options', nor does it mention prerequisites or context for usage. It lacks explicit when/when-not instructions, leaving the agent to infer usage from the tool name alone.

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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