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cancel_dca_order

Cancel existing Dollar-Cost Averaging (DCA) orders to stop automated cryptocurrency purchases at scheduled intervals.

Instructions

Create a DCA order.

Note: Make a single or multiple dca_order_requests 

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cancel_dca_order_requestsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • MCP tool handler for canceling DCA orders. Registers the tool and delegates to ArmorWalletAPIClient.cancel_dca_order, handling errors and authentication.
    @mcp.tool()
    async def cancel_dca_order(cancel_dca_order_requests: CancelDCAOrderRequestContainer) -> List[CancelDCAOrderResponse]:
        """
        Create a DCA order.
    
        Note: Make a single or multiple dca_order_requests 
        """
        if not armor_client:
            return [{"error": "Not logged in"}]
        try:
            result: List[CancelDCAOrderResponse] = await armor_client.cancel_dca_order(cancel_dca_order_requests)
            return result
        except Exception as e:
            return [{"error": str(e)}]
  • Pydantic schemas for CancelDCAOrderRequest (input model with dca_order_id) and CancelDCAOrderResponse (output model with dca_order_id and status).
    class CancelDCAOrderRequest(BaseModel):
        dca_order_id: str = Field(description="id of the DCA order")
    
    
    class CancelDCAOrderResponse(BaseModel):
        dca_order_id: str = Field(description="id of the DCA order")
        status: str = Field(description="status of the DCA order")
  • Container schema for batch input: list of CancelDCAOrderRequest objects.
    class CancelDCAOrderRequestContainer(BaseModel):
        cancel_dca_order_requests: List[CancelDCAOrderRequest]
  • ArmorWalletAPIClient method that performs the HTTP POST to the /transactions/dca-order/cancel/ API endpoint with the list of DCA order IDs.
    async def cancel_dca_order(self, data: CancelDCAOrderRequestContainer) -> List[CancelDCAOrderResponse]:
        """Cancel a DCA order."""
        # payload = [v.model_dump() for v in data.cancel_dca_order_requests]
        payload = data.model_dump(exclude_none=True)['cancel_dca_order_requests']
        return await self._api_call("POST", "transactions/dca-order/cancel/", payload)
  • FastMCP @mcp.tool() decorator registers the cancel_dca_order function as an MCP tool.
    @mcp.tool()
Behavior1/5

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

With no annotations provided, the description carries full burden for behavioral disclosure but fails completely. It doesn't indicate whether this is a read or write operation, what permissions are required, whether the cancellation is reversible, what happens to funds in a DCA order, or what the response looks like. The description actually misrepresents the tool's behavior by suggesting creation rather than cancellation.

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

Conciseness2/5

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

While brief (two sentences), the description is not appropriately structured. The first sentence is factually incorrect, and the second sentence adds confusion rather than clarity. The brevity comes from under-specification rather than efficient communication. The description fails to front-load the actual purpose of the tool.

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?

For a tool that performs a financial operation (canceling DCA orders) with no annotations, 0% schema coverage, and a misleading description, this is completely inadequate. While an output schema exists, the description doesn't provide the basic context needed to understand what the tool does, when to use it, or how to structure the request. The presence of sibling tools like 'cancel_order' and 'list_dca_orders' makes the incompleteness even more problematic.

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?

With 0% schema description coverage and 1 parameter, the description provides no meaningful information about the 'cancel_dca_order_requests' parameter. It mentions 'dca_order_requests' but doesn't explain what this container should contain, how to specify which DCA orders to cancel, or what format the requests should take. The description fails to compensate for the complete lack of schema documentation.

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

Purpose1/5

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

The description 'Create a DCA order' directly contradicts the tool name 'cancel_dca_order', creating confusion about whether this tool creates or cancels orders. The note about 'Make a single or multiple dca_order_requests' further compounds the confusion by suggesting creation rather than cancellation. This is misleading and fails to state the actual purpose of the tool.

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?

No guidance is provided about when to use this tool versus alternatives. The description doesn't mention the sibling tool 'cancel_order' (which might handle different types of orders) or 'list_dca_orders' (which might help identify orders to cancel). There's no indication of prerequisites, timing considerations, or when this tool should not be used.

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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