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nikhil-ganage

MCP Server Airflow Token

get_dag_stats

Retrieve statistics for Apache Airflow DAGs to monitor workflow performance and status.

Instructions

Get DAG stats

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dag_idsNo

Implementation Reference

  • The asynchronous handler function that implements the core logic of the 'get_dag_stats' tool by calling the Airflow DagStatsApi and returning the response as text content.
    async def get_dag_stats(
        dag_ids: Optional[List[str]] = None,
    ) -> List[Union[types.TextContent, types.ImageContent, types.EmbeddedResource]]:
        # Build parameters dictionary
        kwargs: Dict[str, Any] = {}
        if dag_ids is not None:
            kwargs["dag_ids"] = dag_ids
    
        response = dag_stats_api.get_dag_stats(**kwargs)
        return [types.TextContent(type="text", text=str(response.to_dict()))]
  • The get_all_functions function that returns the registration details (function, name, description, read-only flag) for the 'get_dag_stats' tool, imported and used in src/main.py for tool registration.
    def get_all_functions() -> list[tuple[Callable, str, str, bool]]:
        """Return list of (function, name, description, is_read_only) tuples for registration."""
        return [
            (get_dag_stats, "get_dag_stats", "Get DAG stats", True),
        ]
Behavior1/5

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. 'Get DAG stats' provides no information about what the tool actually does behaviorally - whether it's a read-only operation, what permissions might be required, whether it has side effects, what format the statistics are returned in, or any limitations. The description is completely inadequate for understanding the tool's behavior.

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 extremely concise at just three words, which could be appropriate if it were more informative. However, this brevity comes at the cost of being under-specified rather than efficiently informative. There's no structure to speak of - it's a single phrase with no front-loading of critical information.

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

Completeness1/5

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

Given the complexity of DAG systems, the lack of annotations, 0% schema description coverage, and no output schema, the description is completely inadequate. 'Get DAG stats' doesn't explain what statistics are returned, how they're formatted, what the optional dag_ids parameter does, or any behavioral characteristics. For a tool in a complex workflow orchestration system with many similar tools, this minimal description provides insufficient context.

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

Parameters1/5

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

The input schema has 1 parameter (dag_ids) with 0% description coverage, meaning the schema provides no semantic information about this parameter. The description 'Get DAG stats' adds nothing about what the dag_ids parameter does, whether it's required, what format it expects, or how it affects the results. For a tool with undocumented parameters, the description fails to compensate.

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

Purpose2/5

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

The description 'Get DAG stats' is a tautology that essentially restates the tool name. While it indicates the tool retrieves statistics about DAGs (Directed Acyclic Graphs), it lacks specificity about what kind of statistics (e.g., execution metrics, status counts, performance data) or what scope of DAGs it covers. It doesn't differentiate from sibling tools like get_dag, get_dag_details, or get_dag_runs which also retrieve DAG-related information.

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

Usage Guidelines1/5

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. With many sibling tools that retrieve DAG information (get_dag, get_dag_details, get_dag_runs, get_dag_tasks, etc.), there's no indication of what makes this tool unique or when it should be preferred. No context about prerequisites, constraints, or typical use cases is mentioned.

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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