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MCP Server Airflow Token

patch_pool

Modify Airflow pool configurations by updating slot allocation, descriptions, or deferred task handling to optimize workflow resource management.

Instructions

Update a pool

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pool_nameYes
slotsNo
descriptionNo
include_deferredNo

Implementation Reference

  • The async handler function for the 'patch_pool' MCP tool. It constructs a Pool object with optional updates to slots, description, or include_deferred, then calls the Airflow PoolApi to patch the pool and returns the response as text.
    async def patch_pool(
        pool_name: str,
        slots: Optional[int] = None,
        description: Optional[str] = None,
        include_deferred: Optional[bool] = None,
    ) -> List[Union[types.TextContent, types.ImageContent, types.EmbeddedResource]]:
        """
        Update a pool.
    
        Args:
            pool_name: The pool name.
            slots: The number of slots.
            description: The pool description.
            include_deferred: Whether to include deferred tasks in slot calculations.
    
        Returns:
            The updated pool details.
        """
        pool = Pool()
    
        if slots is not None:
            pool.slots = slots
    
        if description is not None:
            pool.description = description
    
        if include_deferred is not None:
            pool.include_deferred = include_deferred
    
        response = pool_api.patch_pool(pool_name=pool_name, pool=pool)
        return [types.TextContent(type="text", text=str(response.to_dict()))]
  • The get_all_functions() function returns a list of tuples for registering MCP tools, including the patch_pool tool with name 'patch_pool', description 'Update a pool', and is_read_only=False.
    def get_all_functions() -> list[tuple[Callable, str, str, bool]]:
        """Return list of (function, name, description, is_read_only) tuples for registration."""
        return [
            (get_pools, "get_pools", "List pools", True),
            (get_pool, "get_pool", "Get a pool by name", True),
            (delete_pool, "delete_pool", "Delete a pool", False),
            (post_pool, "post_pool", "Create a pool", False),
            (patch_pool, "patch_pool", "Update a pool", False),
        ]
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. 'Update a pool' implies a mutation operation, but it doesn't describe what happens during the update—whether it's partial or full, if it requires specific permissions, what the response looks like, or potential side effects. For a mutation tool with zero annotation coverage, this leaves critical behavioral traits unspecified.

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 at three words ('Update a pool'), with no wasted language. It's front-loaded and to the point, making it easy to parse quickly. However, this conciseness comes at the cost of completeness, as noted in other dimensions.

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

Completeness2/5

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

Given the complexity (a mutation tool with 4 parameters), lack of annotations, 0% schema description coverage, and no output schema, the description is incomplete. It doesn't explain what the tool returns, how parameters interact, or the scope of updates. For a tool that modifies resources, this minimal description is inadequate for safe and effective use by an agent.

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%, meaning none of the 4 parameters have descriptions in the schema. The tool description doesn't mention any parameters, so it adds no semantic value beyond what the schema provides. Parameters like 'slots', 'description', and 'include_deferred' are entirely undocumented, leaving their purpose and usage unclear to an agent.

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 'Update a pool' states a clear verb ('Update') and resource ('pool'), which provides basic purpose. However, it doesn't specify what aspects of a pool can be updated or differentiate this tool from sibling tools like 'post_pool' (which likely creates pools) or 'delete_pool' (which removes them). The purpose is understandable but lacks specificity.

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. It doesn't mention prerequisites (e.g., needing an existing pool), when not to use it, or how it differs from similar tools like 'post_pool' (create) or 'delete_pool' (remove). Without this context, an agent might struggle to choose the right tool for modifying pool resources.

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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