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Teradata MCP Server

Official
by Teradata
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Python
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    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

    A3.8/5.0

    Scored across 47 tools

    Disambiguation4/5

    Most tools have clearly distinct purposes with explicit cross-references ('use X instead of Y'), making boundaries clear. Some overlap exists between base_tableUsage and dba_tableUsageImpact (both report table access/user activity), and the dense dba_* and qlty_* families could be confused at a glance, but the descriptions mitigate this well.

    Naming Consistency3/5

    Prefix-based categories (base_, dba_, qlty_, graph_) provide useful structure, but case conventions are inconsistent: camelCase (base_readQuery), PascalCase (rag_Execute_Workflow, sql_Execute_Full_Pipeline), and snake_case (plot_polar_chart) are all mixed. Verb usage also varies, with some names lacking verbs entirely (base_columnDescription), though the names remain readable overall.

    Tool Count2/5

    47 tools is well into 'tool sprawl' territory and exceeds the 25+ threshold for a heavy surface. The server bundles many distinct modules (RAG, SQL clustering, plotting, graph lineage, DBA, security, quality) into one MCP server, which makes scanning and selecting the right tool difficult. The count reflects breadth, but the scope is too large for a single coherent toolset.

    Completeness4/5

    For an analytics/observability-focused server, coverage is extensive: querying, metadata exploration, DBA monitoring, security permissions, data quality, lineage analysis, charting, and RAG workflows are all represented. The main gaps are write operations (no data modification, DDL execution, or user/role management), but these appear deliberately out of scope. Minor gaps like bulk export or scheduling are workable around.

    Maintenance

    ActivityActive
    ResponsivenessWithin a week

    Resources