Metorial MCP. Control and monitor your entire serverless AI lifecycle.

Listen up. This isn't another fancy dashboard; this is pure visibility into your agent’s compute layer. The Metorial MCP Server gives you the controls to manage remote serverless AI lifecycles, treating complex agent logic like a managed cloud service so you don't lose track of what's running or where your budget's going.

You need full command over your resources—from spinning up new compute matrices to shutting down endpoints that’ve gone stale. You can use it to monitor execution traces, nail down resource costs, and strictly enforce boundaries on whatever complex AI tooling you deploy. Here's what you can do with the tools available:

To Manage Your Compute Environment:
When you need your agent logic running somewhere remote, you’ll initiate a new environment using metorial_deploy_server. This action initializes that serverless compute matrix for your agent's specific needs. You keep tabs on what's active by calling metorial_list_servers, which gives you a complete inventory of every single MCP bound running in the entire Metorial workspace, whether it’s currently up or shut down.

If an endpoint is running and you gotta know if it’s healthy, you check its operational status using metorial_get_server_status; that tool pulls the current health metrics for any specific node you point it at.

To Execute and Isolate Tasks:
If you want to run a specific piece of logic without spinning up a whole new server, you can use metorial_invoke_server_tool. This runs an isolated tool schema inside a designated serverless container. For complex workflows, you’ll need to manage the lifecycle; once a server is done with its job and isn't needed anymore, you terminate it completely by running metorial_delete_server, which stops and removes that logical server instance from your workspace.

To Audit Performance and Usage:
Tracking performance metrics and costs is critical. You run metorial_get_usage_metrics to aggregate all the data on latency, token usage, and actual costs across every single agent running in your entire workspace. For a deep dive into how an agent actually got its results, you pull detailed execution paths using metorial_get_trace_details; this provides a line-by-line breakdown of a specific tool run, showing exactly where the performance bottlenecks hit.

When you need to see what agents have been running and what tools they used over time, you use metorial_list_traces, which pulls a full log list of all past agent executions.

In short: You manage deployment with metorial_deploy_server and clean up with metorial_delete_server. You check the pulse using metorial_get_server_status, you run specific code with metorial_invoke_server_tool, and you audit everything—the cost, the steps, and the history—using metorial_get_usage_metrics and metorial_list_traces. It's your full operational command center for agent scaling.