Skip to main content
Glama
25andresbernal

semantic-model-kit

by 25andresbernal
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Related Servers

Alternatives to semantic-model-kit

No user-submitted related servers found.

    Related Servers

    • io.github.mischuh/canonic

      mischuh
      F
      license
      Not graded
      quality
      A
      maintenance
      Enables AI agents to query databases correctly by serving governed context over MCP, including canonical metric definitions, relationships, business meaning, and guardrails, so answers come back with resolved definitions, fired filters, freshness, and a trust score instead of guesses.
      3
      -

    • data-analyst

      AHMED-DEEBB
      A
      license
      Not graded
      quality
      C
      maintenance
      Enables MCP clients to ask bilingual data questions, inspect database tables and schemas, and execute validated read-only SQL through exposed tools.
      MIT

    • dbtBigQuery

      FelipeRego
      A
      license
      A
      quality
      B
      maintenance
      Enables LLM agents to query governed GA4 metrics over MCP, returning definitions, assumptions, and failure modes from a MetricFlow semantic layer without writing SQL.
      7
      MIT

    • Vela MCP Server

      natori-hrj
      A
      license
      Not graded
      quality
      D
      maintenance
      Enables governed, agent-agnostic data exploration by allowing users to ask natural language questions through MCP-compatible agents, executing safe, permission-scoped queries against data sources and returning interactive charts.
      3 npm
      Apache 2.0

    • agent-gateway

      yongpange
      F
      license
      A
      quality
      C
      maintenance
      Enables AI agents to discover and query enterprise knowledge documents and business metrics through standardized MCP tools, supporting retrieval, structured sales/order analysis, anomaly scanning, and data lineage lookups.
      12
      -
    Deploy Server
    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

    A4.4/5.0

    Scored across 8 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose: listing entities/metrics, describing them in detail, querying metrics, free-text searching, explaining join paths, and retrieving verified questions. There is no overlap or ambiguity between tools; an agent can reliably select the right one for a given task.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun (or verb_noun_noun) pattern in snake_case, such as list_entities, describe_entity, query_metric, and explain_join. The naming is uniform and predictable, with no mixed conventions or vague verbs.

    Tool Count5/5

    With 8 tools, the server is well-scoped for its purpose of exploring and querying a semantic model. Each tool serves a necessary function—discovery, description, querying, search, join explanation, and regression testing—and none feels redundant or missing.

    Completeness5/5

    The tool surface fully covers the domain of semantic model interaction: listing and describing entities and metrics, querying metrics with validation, searching across all semantic elements, explaining join paths, and checking verified questions. There are no obvious gaps; even potential needs like filtering dimensions are handled within query_metric and describe_entity.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources