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Alternatives to slh-server

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    Related Servers

    • A
      license
      A
      quality
      C
      maintenance
      Enables AI agents and their users to ask business questions in plain language and receive governed metric values, breakdowns, share-of-total figures, and the exact parameterized SQL behind them, drawn from a single semantic model rather than ad-hoc SQL. It also lets clients inspect available metrics and their definitions, and preview optimized queries without executing them.
      4
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables agents to answer analytics questions with provable correctness by resolving metrics through the semantic layer, tracing lineage, traversing knowledge graphs, checking freshness, and searching glossary definitions, while enforcing access controls and logging every call.
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables AI agents to retrieve governed business metric definitions and compiled SQL through MCP-style tools, so they can query consistently under their own read-only roles without guessing joins or metric logic.
      MIT

    TDQS

    A3.9/5.0

    Scored across 5 tools

    Disambiguation5/5

    Each tool targets a distinct step of the metrics workflow: listing metrics, listing dimensions, describing a single metric, compiling a query without running it, and executing a query. The boundaries between describe_metric and explain_metric_query are clear from their descriptions.

    Naming Consistency5/5

    All five tools follow a consistent snake_case verb_noun pattern (list_metrics, list_dimensions, describe_metric, explain_metric_query, query_metric). The convention is predictable throughout.

    Tool Count5/5

    Five tools is well-scoped for a governed metrics query interface. Each tool earns its place across the discover-explain-execute workflow with no redundancy.

    Completeness4/5

    The surface covers the full read lifecycle: discovering metrics and dimensions, inspecting definitions, dry-running a query, and executing it. Minor gaps exist (e.g., no explicit tool for listing available time ranges or inspecting the row limit/cost cap), but agents can work around these.