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io.github.Aguantar/clickhouse-dataops-mcp

by Aguantar
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Python
Hybrid

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    TDQS

    A4.2/5.0

    Scored across 8 tools

    Disambiguation5/5

    Each tool has a distinct purpose: data quality checks, disk usage, query analysis, table listing, pipeline latency, query execution, slow query diagnosis, and schema details. No two tools overlap in functionality; descriptions clearly differentiate them.

    Naming Consistency4/5

    All tools share the 'ch_' prefix. Most use a verb_noun pattern (e.g., ch_list_tables, ch_explain_query), but 'ch_query' is just a noun and 'ch_slow_queries' is adjective_noun, causing minor inconsistency. Overall pattern is clear.

    Tool Count5/5

    With 8 tools, the server covers a focused data operations scope without being overloaded. Each tool addresses a specific need, and the count feels well-scoped for managing and optimizing ClickHouse.

    Completeness5/5

    The tool set covers essential dataops tasks: exploring tables, checking schema, running queries, analyzing slow queries, explaining plans, monitoring pipeline latency, checking data quality, and disk usage. No obvious gaps for a read-optimized monitoring tool.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources