MCP Tools
MCP Tools
- list_node_types_that_support_metrics: List node types supporting metrics retrieval
- list_available_metrics_for_node: List available metrics for a specific node
- get_metrics_for_node: Get metrics data for a resource using its node ID
- get_anomalies_for_metrics: Detect anomalies in metric data
Configuration
Configure the Metrics plugin by runninguv run unpage configure
or by editing
the ~/.unpage/profiles/<profile_name>/config.yaml
file:
Tools
The Metrics plugin provides the following tools to Agents and MCP Clients:list_node_types_that_support_metrics
List the types of nodes that support metrics retrieval.Arguments
NoneReturns
list[string]
: Node types in the format “source:type” (e.g., “aws:ec2_instance”, “kubernetes:pod”) that support metrics collection.list_available_metrics_for_node
List the available metrics for a specific node.ArgumentsReturns
The unique identifier of the node to check for available metrics.
list[string]
or string
: List of available metric names for the node, or an error message if the node doesn’t support metrics.get_metrics_for_node
Get metrics data for a resource using its node ID.ArgumentsReturns
The unique identifier of the node to retrieve metrics for.
The starting time for the metrics range (ISO 8601 timestamp). A good starting point is one hour before the current time.
The ending time for the metrics range (ISO 8601 timestamp).
List of specific metric names to retrieve. If not provided, all available metrics for the node will be returned. Can also be provided as a JSON string array.
list[dict]
or string
: Metric observations with timestamps and values, or an error/informational message.Note: Use list_available_metrics_for_node
first to get valid metric names for the node.get_anomalies_for_metrics
Detect anomalies in metric data using statistical analysis.ArgumentsReturns
List of data points, where each entry contains a timestamp and value pair. Typically obtained from the results of
get_metrics_for_node
.list[dict]
: Detected anomalies in the metric data, including anomaly details and confidence scores.