zapcap_mcp_monitor_task
Monitor video processing task progress in the ZapCap API by tracking video and task IDs to check completion status.
Instructions
Monitor task progress
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| request | Yes |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Implementation Reference
- src/zapcap_mcp_server/server.py:156-167 (handler)The handler function decorated with @mcp.tool that implements the logic to monitor the task progress by making a GET request to the ZapCap API using the provided video_id and task_id.
@mcp.tool(description="Monitor task progress") def zapcap_mcp_monitor_task(request: MonitorTask) -> Dict[str, Any]: headers = {"x-api-key": get_api_key()} with httpx.Client() as client: response = client.get( f"https://api.zapcap.ai/videos/{request.video_id}/task/{request.task_id}", headers=headers ) response.raise_for_status() return response.json() - Pydantic BaseModel defining the input parameters for the zapcap_mcp_monitor_task tool: video_id and task_id.
class MonitorTask(BaseModel): video_id: str = Field(description="Video ID") task_id: str = Field(description="Task ID") - Helper function to retrieve and validate the ZAPCAP_API_KEY from environment variables, used by the tool handler.
def get_api_key() -> str: api_key = os.getenv("ZAPCAP_API_KEY") if not api_key: raise ValueError("ZAPCAP_API_KEY environment variable is required") return api_key