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Glama

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Alternatives to Prism

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    Related Servers

    • A
      license
      Not graded
      quality
      D
      maintenance
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      ISC
    • A
      license
      Not graded
      quality
      C
      maintenance
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      1
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    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables asking product analytics questions in English and receiving answers as real SQL along with the resulting numbers, through read-only MCP tools for funnels, retention, trends, paths, and event counts.
      249 npm
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    • A
      license
      Not graded
      quality
      F
      maintenance
      Enables natural language data queries with statistical rigor and a semantic layer, supporting 23+ analytical tools for product analytics.
      1
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    • 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
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    • A
      license
      A
      quality
      A
      maintenance
      Agent-native semantic layer, letting AI agents query databases through specifying intent instead of writing SQL, then compiling structured queries into correct, dialect-aware SQL. Dynamic and expressive, supporting multi-stage queries, time-shifts, and complex join schemas.
      20
      1,223 PyPI
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    TDQS

    A4.2/5.0

    Scored across 11 tools

    Disambiguation4/5

    Most tools target distinct resources (projects, customers, errors, hosts, revenue, costs). However, get_customer and open_moat_dashboard both return overlapping customer info (payment, entitlements, read-cost) with different output formats, which could cause selection ambiguity.

    Naming Consistency4/5

    Tools follow a consistent imperative_underscore pattern with verbs like get_, list_, draw_, open_, and use_. The naming is clear and predictable, though the verb choice varies appropriately across actions.

    Tool Count5/5

    With 11 tools covering project management, customer insights, error analysis, host analytics, and revenue/costs, the set is well-scoped for a product analytics platform. Each tool serves a distinct purpose without redundancy.

    Completeness4/5

    The core analytics needs are covered: customer overview, error impact, traffic analysis, revenue tracking, and cost breakdown. Minor gaps like user-level event logs or data export are acceptable given the server's focus on cross-layer analytics.

    Maintenance

    ActivitySlowing
    ResponsivenessNo issues