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Observability MCP Server

by sandraschi
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Python
Hybrid

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    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    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

    Resources