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Databricks Spark Analyzer MCP

by ibenlems
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    Shareable, User-Owned Context BackendPeople, applications, and agents read, write, and coordinate together in one context lake. Reach it over MCP, a CLI, a Python SDK, or REST.fulcradynamics.comAd

    TDQS

    C2.7/5.0

    Scored across 5 tools

    Disambiguation2/5

    diagnose_job is explicitly documented as an alias for analyze_run, so two tools do the same thing by design. analyze_run (metrics supplied by caller) vs analyze_run_by_id (fetches the run) and fetch_job_run_context (returns raw-ish context that analyze_run_by_id also fetches internally) create overlapping boundaries an agent must infer from subtle wording.

    Naming Consistency4/5

    All names are snake_case verb_noun (analyze_run, analyze_run_by_id, analyze_stage, fetch_job_run_context, diagnose_job), which is predictable. The only wrinkle is diagnose_job, whose verb implies a different action than the analyze_* family despite being an alias.

    Tool Count4/5

    Five tools is a tight, well-scoped set for Spark run analysis. The only waste is diagnose_job occupying a slot purely as a duplicate of analyze_run, leaving four effectively unique operations.

    Completeness3/5

    The analysis lifecycle is covered from raw metrics, run id, context fetch, and single-stage deep dive, but there is no way to discover run or job ids (no list_jobs/list_runs), which blocks analyze_run_by_id for an agent starting cold. Cross-run comparison or recommendation export is also absent.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

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