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Glama
sandraschi

Observability MCP Server

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
PROMETHEUS_PORTNoPrometheus metrics server port9090
OTEL_SERVICE_NAMENoOpenTelemetry service nameobservability-mcp
METRICS_RETENTION_DAYSNoMetrics retention period (days)30
OTEL_EXPORTER_OTLP_ENDPOINTNoOTLP exporter endpoint (optional)

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": true
}
logging
{}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
extensions
{
  "io.modelcontextprotocol/ui": {}
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
check_stack_statusA

Check the health and connectivity of the observability stack (Loki, Prometheus, Grafana).

This diagnostic tool verifies if the underlying Docker-based services are reachable and correctly configured.

manage_alert_configsA

Manage metric alert configurations.

Operations:

  • list: List all current alert configurations

  • add: Add a new alert configuration (requires 'alert' dict)

  • remove: Remove an alert by metric name (requires 'metric_name')

  • toggle: Enable/disable an alert (requires 'metric_name')

show_status_dashboardA

Display a rich, premium status dashboard using Prefab UI. Provides a visual overview of stack health, system metrics, and active alerts. Hardened: operates in Degraded Mode when Docker or stack services are unreachable.

monitor_server_healthB

Perform real-time health check on an MCP server or web service.

This tool uses OpenTelemetry for metrics collection and provides comprehensive health monitoring with detailed response analysis.

collect_performance_metricsA

Collect comprehensive performance metrics for the system or specific service.

Uses OpenTelemetry for structured metrics collection and psutil for system monitoring. Metrics are persisted for historical analysis and trend detection.

trace_mcp_callsA

Record a trace for MCP call monitoring and distributed tracing.

Creates OpenTelemetry spans for tracking MCP server interactions, enabling distributed tracing across multiple MCP servers.

generate_performance_reportsB

Generate comprehensive performance reports with automated analysis.

Analyzes historical metrics data to provide insights, trends, and recommendations for optimizing MCP server performance.

alert_on_anomaliesB

Monitor for performance anomalies and trigger alerts.

Uses statistical analysis and configurable thresholds to detect anomalies in MCP server performance metrics and trigger appropriate alerts.

monitor_system_resourcesB

Monitor system-wide resources and provide real-time status.

Collects comprehensive system resource information including CPU, memory, disk, network, and process statistics for overall system health monitoring.

analyze_mcp_interactionsB

Analyze patterns in MCP server interactions and usage.

Examines trace data and interaction patterns to provide insights into how MCP servers are being used and identify optimization opportunities.

export_metricsB

Export collected metrics in various formats for external monitoring systems.

Supports Prometheus, OpenTelemetry, and JSON formats for integration with existing monitoring infrastructure.

send_logs_to_lokiB

Send custom log entries to Loki for centralized log aggregation.

This tool allows manual log injection into the Loki log aggregation system, enabling correlation between custom events and system metrics.

query_loki_logsC

Query logs from Loki with advanced filtering and analysis.

This tool provides powerful log querying capabilities using Loki's LogQL syntax, enabling correlation between logs, metrics, and traces.

analyze_log_patternsB

Analyze log patterns and anomalies using Loki queries.

This tool identifies common log patterns, error spikes, and potential issues by analyzing log data over time windows.

correlate_logs_and_metricsB

Correlate log entries with metrics for comprehensive incident analysis.

This tool combines Loki log querying with Prometheus metrics to provide unified observability insights and root cause analysis.

manage_grafana_dashboardsA

Manage Grafana dashboards.

Operations:

  • list: List all dashboards

  • create: Create or update a dashboard (requires 'dashboard_json')

  • delete: Delete a dashboard by UID (requires 'uid')

  • get: Get dashboard JSON by UID (requires 'uid')

manage_grafana_datasourcesA

Manage Grafana datasources.

Operations:

  • list: List all datasources

  • add: Add a new datasource (requires 'ds_json')

  • remove: Remove a datasource by ID (requires 'ds_id')

  • test: Test connection to a datasource by ID (requires 'ds_id')

provision_standard_dashboardsB

Provision standard SOTA industrial dashboards to Grafana.

This tool sets up pre-configured dashboards for:

  1. System Health & Resource Usage

  2. MCP Trace Analytics

  3. Loki Log Central

agentic_observability_workflowB

Multi-step observability triage (stack → logs → health).

Uses host LLM sampling when available; otherwise runs a fixed tool chain.

observability_stack_health_cardC

Prefab card: unified PLG stack health (Grafana, Prometheus, Loki).

observability_grafana_dashboards_cardB

Prefab card: Grafana dashboard list (read-only).

observability_capabilities_cardC

Prefab card: SOTA manifest and tool surface summary.

Prompts

Interactive templates invoked by user choice

NameDescription
observability_getting_started
incident_triage

Resources

Contextual data attached and managed by the client

NameDescription
observability_capabilities
observability_sota_manifest
observability_prefab_manifest
observability-expert/SKILL.mdObservability-MCP expert skill
observability-expert/_manifestFile listing for observability-expert

TDQS

B3/5.0

Scored across 22 tools

Disambiguation2/5

Several clusters of overlapping tools exist: check_stack_status, show_status_dashboard, observability_stack_health_card, and monitor_server_health all report health; collect_performance_metrics, monitor_system_resources, and generate_performance_reports all gather/analyze metrics; query_loki_logs, analyze_log_patterns, and correlate_logs_and_metrics all touch Loki. Descriptions add nuance but an agent can easily pick the wrong status/health/report tool.

Naming Consistency4/5

Nearly all tools follow a clean verb_noun pattern (check_stack_status, query_loki_logs, manage_grafana_dashboards). The main deviation is the trailing observability_*_card tools plus agentic_observability_workflow, which are noun-style and break the pattern, though they read as a deliberate UI-card group.

Tool Count3/5

22 tools is at the heavy end for an observability server; the core workflows (metrics, logs, traces, dashboards) are covered, but multiple near-duplicate reporting/status/card tools push it beyond a tight scope.

Completeness4/5

Strong coverage of the PLG stack: log ingestion/query/analysis, trace recording, alert config management, Grafana dashboard and datasource CRUD, and metric export. The notable gap is direct Prometheus/PromQL metric querying for ad-hoc exploration, plus no update/delete for dashboards beyond create-or-update.

Maintenance

ActivityActive
ResponsivenessNo issues