The Job Management server provides the operational interface between your AI agents and the IDMC execution engine. It allows agents to orchestrate data pipelines, monitor process health, and manage task lifecycles in real time.
By exposing Data Integration controls, this MCP server enables your AI to act as an automated operator, ensuring that jobs are triggered, tracked, and managed without manual intervention.
Key capabilities
This MCP server offers the following key capabilities:
•Automated orchestration. Enables agents to trigger complex mapping tasks dynamically.
•Active job control. Provides the ability to immediately halt or stop tasks, ensuring rapid responses to identified data issues or resource constraints.
•Operational visibility. Retrieves live execution status and performance metrics, like rows processed or success/failure status, for any active job.
Use cases
Use this MCP server to address the following use cases:
•Self-healing pipelines. Allows AI agents to monitor a failed job and automatically attempt a restart or trigger a notification based on the error log.
•Job monitoring. Allows users to ask the AI for a report on their daily data loads without logging into IDMCMonitor.
•Event-driven integration. Enables AI agents to trigger specific data loads automatically when a business event, like a new file arrival, is detected.
•Resource optimization. Enables AI agents to identify and stop long-running or stalled jobs that are consuming excessive Secure Agent resources.
Tools
The following table describes the tools available on this MCP server:
Tools
Description
get_job_status
Retrieves real-time execution states and operational statistics, including row counts and error logs, for a specific job.
run_mapping_task
Triggers job execution by initiating a specific mapping task with the required runtime parameters.
stop_running_job
Sends an immediate termination request to the Secure Agent to halt the execution of an active task.
Note:
In AI Agent Engineering, the MCP server tools are called "actions."