Skip to main content
Glama
MostafaAI10

OmniData MCP

by MostafaAI10
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Related Servers

Alternatives to OmniData MCP

No user-submitted related servers found.

    Related Servers

    • Databricks MCP Server

      OliveriGuido
      F
      license
      A
      quality
      D
      maintenance
      A Model Context Protocol server that lets LLM clients answer business questions in natural language over a Databricks dataset without writing SQL by hand.
      1
      -

    • duckdb-analytics-mcp

      arthurxavier106
      A
      license
      A
      quality
      C
      maintenance
      A read-only DuckDB MCP server offering context-efficient analytics tools (list_datasets, describe_table, profile_column, explain, query) with a semantic layer for business rules, security guards, and disclosed truncation to help LLMs produce correct answers while minimizing token usage.
      5
      MIT

    • DuckDB MCP Server

      mustafahasankhan
      A
      license
      Not graded
      quality
      D
      maintenance
      A Model Context Protocol server implementation that connects AI assistants to DuckDB, enabling them to query and analyze data from various sources including CSV, Parquet, JSON, and cloud storage through SQL.
      18
      MIT

    • Semantic BI MCP

      LovroBubanic
      F
      license
      Not graded
      quality
      D
      maintenance
      Model Context Protocol (MCP) server that gives AI assistants a safe, correct data-analyst capability over business metrics - without raw SQL improvisation.
      -
    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

    A4.5/5.0

    Scored across 8 tools

    Disambiguation5/5

    Each tool targets a distinct concern: health check, dataset discovery, schema introspection, row counting, SQL queries, profiling, charting, and declarative transformations. Even run_sql_query vs generate_chart are clearly differentiated by output type (structured data vs. image).

    Naming Consistency4/5

    The naming convention is predominantly verb_noun (list_datasets, get_schema, run_sql_query, generate_chart, execute_pyspark_job). The sole outlier is health_check, which follows noun_verb order and is not consistent with the others.

    Tool Count5/5

    With 8 tools, the server is well-scoped for a database analytics MCP. Each tool covers a genuine need without redundancy or bloat, staying within the ideal 3-15 range.

    Completeness5/5

    The tool surface covers the full analytical lifecycle: connect/health, explore structure, query, profile, chart, and transform. While data modification is intentionally absent, all read-only analysis needs are addressed without obvious gaps.

    Maintenance

    ActivitySlowing
    ResponsivenessNo issues

    Resources