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nikhil-ganage

MCP Server Airflow Token

get_import_errors

Retrieve and list import errors from Apache Airflow deployments to identify and resolve DAG loading issues.

Instructions

List import errors

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
offsetNo
order_byNo

Implementation Reference

  • The async handler function for the 'get_import_errors' MCP tool. It constructs kwargs from optional parameters (limit, offset, order_by), calls ImportErrorApi.get_import_errors, converts the response to dict string, and returns it wrapped in types.TextContent.
    async def get_import_errors(
        limit: Optional[int] = None,
        offset: Optional[int] = None,
        order_by: Optional[str] = None,
    ) -> List[Union[types.TextContent, types.ImageContent, types.EmbeddedResource]]:
        # Build parameters dictionary
        kwargs: Dict[str, Any] = {}
        if limit is not None:
            kwargs["limit"] = limit
        if offset is not None:
            kwargs["offset"] = offset
        if order_by is not None:
            kwargs["order_by"] = order_by
    
        response = import_error_api.get_import_errors(**kwargs)
        return [types.TextContent(type="text", text=str(response.to_dict()))]
  • Registration function that provides the tuple for registering the 'get_import_errors' tool: (function, name, description, read_only). This is likely used by the main MCP server to register the tool.
    def get_all_functions() -> list[tuple[Callable, str, str, bool]]:
        """Return list of (function, name, description, is_read_only) tuples for registration."""
        return [
            (get_import_errors, "get_import_errors", "List import errors", True),
            (get_import_error, "get_import_error", "Get a specific import error by ID", True),
        ]
Behavior1/5

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

No annotations are provided, so the description must fully disclose behavioral traits. It only states 'List import errors' without any details on permissions, rate limits, pagination, or what the output looks like (e.g., format, error types). This is inadequate for a tool with parameters and no output schema.

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 extremely concise with 'List import errors', a single phrase that front-loads the core purpose without unnecessary words. It efficiently communicates the basic action, though this brevity contributes to gaps in other dimensions.

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 (3 parameters, 0% schema coverage, no output schema, no annotations), the description is severely incomplete. It lacks details on behavior, parameters, output, and usage context, making it insufficient for an AI agent to effectively invoke the tool without guesswork.

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?

Schema description coverage is 0%, so the description must compensate by explaining parameters. It provides no information about 'limit', 'offset', or 'order_by', leaving their purposes and usage undocumented. This fails to add meaning beyond the bare schema, resulting in poor parameter guidance.

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

Purpose3/5

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

The description 'List import errors' clearly states the action (list) and resource (import errors), making the purpose understandable. However, it lacks specificity about what import errors are (e.g., from data imports in Airflow) and doesn't distinguish it from sibling tools like 'get_import_error' (singular), leaving ambiguity about scope.

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. It doesn't mention sibling tools like 'get_import_error' (singular) or other error-related tools, nor does it specify prerequisites or contexts for usage, leaving the agent to infer based on tool names 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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