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Glama

Related Servers

Alternatives to Spoor

No user-submitted related servers found.

    Related Servers

    • A
      license
      A
      quality
      A
      maintenance
      Enables diagnosing long AI-agent traces by identifying the most likely root-cause steps, so users can quickly find where an agent run went wrong.
      1
      34 PyPI
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables agents to trace upstream dependencies and downstream blast radius across a Neo4j-modeled service topology, rank root-cause hypotheses, and score composite risk from graph, temporal, git-diff, and trace signals. It also stages rollback-ready remediation plans that execute only after human approval and the required scope.
      Apache 2.0
    • F
      license
      Not graded
      quality
      A
      maintenance
      Enables users to investigate infrastructure incidents in plain English, correlate observability and deploy data with runbooks, and get evidence-backed root-cause proposals with approval-gated remediation.
      5
      -
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables AI agents to perform observability analysis across Prometheus-compatible metrics and logs without prior vendor knowledge, including signal discovery, entity topology resolution, root-cause analysis, correlation, and capacity forecasting through natural-language anchors.
      Apache 2.0
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables a local AI agent to investigate service incidents by querying read-only telemetry such as service health, metrics, logs, and traces, then produce verifiable assessments with evidence links and uncertainty.
      Apache 2.0
    • A
      license
      Not graded
      quality
      D
      maintenance
      Enables autonomous SRE incident investigation by allowing users to describe incidents in natural language. The agent follows a governed state machine to gather read-only evidence and produce grounded conclusions.
      MIT

    TDQS

    A3.6/5.0

    Scored across 4 tools

    Disambiguation4/5

    Each tool maps to a distinct signal scope: list_services inventories services, new_errors is log-specific, trace_diff is trace-specific, and what_changed is the cross-signal ranked entry point. The only soft overlap is that what_changed subsumes findings from new_errors and trace_diff, but its 'start here' framing and ranked-summary output keep the boundary workable.

    Naming Consistency3/5

    All names use snake_case casing, but the conventions are mixed: list_services is verb_noun, new_errors is adjective_noun, trace_diff is noun_noun, and what_changed is a question-phrase. Readable and memorable, but there is no predictable verb_noun pattern an agent can rely on.

    Tool Count5/5

    Four tools for a baseline-diffing observability server is well-scoped: one inventory, one per signal (logs, traces), and one aggregator. Each tool clearly earns its place with no gratuitous or redundant entries.

    Completeness4/5

    The surface covers service inventory, log errors, trace regressions, and a unified change ranking, which handles the core diagnostic workflow. Gaps are minor: no metric-specific diff tool (metrics only surface inside what_changed), no raw log/span search, and no way to configure the comparison window or baseline.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues