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run_workflow

Execute workflows using natural language prompts by specifying project, domain, and inputs to automate tasks and processes.

Instructions

Run a workflow with natural language.

- Based on the prompt and inputs dictionary, determine the workflow to run - Format the inputs dictionary so that it matches the workflow function signature - Invoke the workflow Args: project: Project to run the workflow in. domain: Domain to run the workflow in. name: Name of the task to run. inputs: A dictionary of inputs to the workflow. Returns: A dictionary of outputs from the workflow.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainYes
inputsYes
nameYes
projectYes

Implementation Reference

  • The handler function decorated with @mcp.tool(), implementing the run_workflow tool. It executes a workflow in Union using the provided inputs, waits for completion, and returns the outputs and console URL.
    @mcp.tool() @require_auth def run_workflow( name: str, inputs: dict, project: str, domain: str, ctx: Context, ) -> tuple[dict, str]: """Run a workflow with natural language. - Based on the prompt and inputs dictionary, determine the workflow to run - Format the inputs dictionary so that it matches the workflow function signature - Invoke the workflow Args: project: Project to run the workflow in. domain: Domain to run the workflow in. name: Name of the task to run. inputs: A dictionary of inputs to the workflow. Returns: A dictionary of outputs from the workflow. """ print(f"Running workflow {name} in project {project} and domain {domain}") remote = _remote(project, domain) workflow = remote.fetch_workflow(project=project, domain=domain, name=name) execution = remote.execute(workflow, inputs, project=project, domain=domain) execution = remote.wait(execution, poll_interval=timedelta(seconds=2)) outputs = {k: v for k, v in execution.outputs.items() if v is not None} url = remote.generate_console_url(execution) return outputs, url

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