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jupiterbak

AYX-MCP-Wrapper

by jupiterbak

start_workflow_execution

Initiate workflow execution by ID with input data to create a queued job and obtain a job ID for tracking.

Instructions

Start a workflow execution by its ID and return the job ID. This will create a new job and add it to the execution queue. This call will return a job ID that can be used to get the job details later. The input data is a list of name-value pairs, each containing a name and value.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
workflow_idYes
input_dataNo

Implementation Reference

  • Core handler function in AYXMCPTools class that validates input, enqueues the workflow job using Alteryx API, and returns the job response.
    def start_workflow_execution(self, workflow_id: str, input_data: list[InputData] = None):
        """Start a workflow execution by its ID and return the job ID. This will create a new job and add it to the execution queue.
        This call will return a job ID that can be used to get the job details. Once the job is executed, 
        the results can be retrieved via the produced JobID
        The input data is a list of name-value pairs, each containing a name and value."""
        try:
            workflow = self.workflows_api.workflows_get_workflow(workflow_id)
            if not workflow:
                return "Error: Workflow not found"
            questions = self.workflows_api.workflows_get_workflow_questions(workflow_id)
            if (not questions or len(questions) == 0) and (input_data):
                return "Error: Workflow has no questions, input data not allowed"
            if questions and len(questions) > 0:
                for question in questions:
                    if question.name not in [item.name for item in input_data]:
                        return f"Error: Input data must contain the question '{question.name}'"
                    
            # Convert InputData objects to AppValue objects
            app_values = None
            if input_data:
                app_values = [server_client.AppValue(name=item.name, value=item.value) for item in input_data]
                    
            # Proper type conversion
            workflow = server_client.WorkflowView(workflow)
            contract = server_client.EnqueueJobContract(worker_tag=workflow.worker_tag, questions=app_values)
            api_response = self.workflows_api.workflows_enqueue(workflow_id, contract)
            return pprint.pformat(api_response)
        except ApiException as e:
            return f"Error: {e}"
  • Pydantic BaseModel defining the structure for input data parameters (list of name-value pairs) used in workflow execution.
    class InputData(BaseModel):
        name: str
        value: str
  • MCP tool registration decorator (@app.tool()) that wraps and delegates to the core handler in tools.py.
    @self.app.tool()
    def start_workflow_execution(workflow_id: str, input_data: list[InputData] = None):
        """Start a workflow execution by its ID and return the job ID. 
        This will create a new job and add it to the execution queue.
        This call will return a job ID that can be used to get the job details later. 
        The input data is a list of name-value pairs, each containing a name and value."""
        return self.tools.start_workflow_execution(workflow_id, input_data)
Behavior2/5

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

With no annotations, the description carries full burden. It discloses that execution creates a job and adds it to a queue, which implies mutation and asynchronous behavior. However, it lacks details on permissions, rate limits, error handling, or what 'start' entails operationally (e.g., immediate vs. scheduled).

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 front-loaded with the core action but includes redundant phrasing ('This call will return...' repeats earlier info). Sentences are mostly efficient, but some repetition reduces conciseness without adding value.

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?

For a mutation tool with no annotations and no output schema, the description is incomplete. It covers basic purpose and parameter hints but lacks behavioral details (e.g., side effects, response format beyond job ID), usage context, and error handling, which are critical for an agent to invoke it correctly.

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?

Schema description coverage is 0%, so the description must compensate. It explains 'input_data' as 'a list of name-value pairs', adding meaning beyond the schema's structural definition. However, it doesn't clarify the purpose or format of 'workflow_id' or provide examples, leaving gaps for the two parameters.

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 action ('Start a workflow execution by its ID') and the resource ('workflow'), with a specific outcome ('return the job ID'). It distinguishes from sibling tools like 'execute_workflow_with_monitoring' by focusing on initiation rather than monitoring, though the distinction could be more explicit.

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 like 'execute_workflow_with_monitoring'. The description mentions queuing and job ID retrieval, but lacks explicit context, prerequisites, or exclusions for usage.

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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