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Observability MCP Server

by Pawangunjkar
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Python
Remote

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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

    B3.1/5.0

    Scored across 11 tools

    Disambiguation5/5

    Each tool targets a distinct observability resource and action: connection, status, metrics queries, error-rate specialization, log queries, trace retrieval/search, Grafana datasource/alert listing, and annotation writing. While query_metrics and error_rate both touch metrics, error_rate is narrowly scoped to 5xx rates over a fixed window, and get_trace vs search_traces differ by lookup key vs tags, leaving no ambiguous boundaries.

    Naming Consistency5/5

    All tools share the obs_ prefix, followed by a verb_noun or clear verb pattern (e.g., obs_query_metrics, obs_get_trace, obs_grafana_datasources, obs_annotate). The naming is predictable and uniform, with no mixed casing or inconsistent verb styles, making tool selection straightforward.

    Tool Count5/5

    With 11 tools, the server covers a broad but well-scoped observability surface—metrics, logs, traces, and Grafana integration—without feeling bloated. Each tool serves a distinct purpose and earns its place, fitting within the ideal 3-15 tool range.

    Completeness4/5

    The tool set provides solid read coverage for metrics (instant queries, error rates), logs, traces (get/search), and Grafana metadata (datasources, alerts), plus one write operation (annotations). Minor gaps exist, such as missing range queries for Prometheus or alert management write operations, but the core observability workflows are well represented and agents can accomplish typical tasks without dead ends.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources