Skip to main content
Glama
Giorgio089

dataframe-detective-mcp

by Giorgio089
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Related Servers

Alternatives to dataframe-detective-mcp

No user-submitted related servers found.

    Related Servers

    • mcp-pandas

      devantage
      A
      license
      A
      quality
      C
      maintenance
      Enables data analysis on CSV/Excel files using pandas. Supports profiling, column interpretation, sandboxed code execution, and interactive chart generation.
      4
      1
      MIT

    • CSV MCP Assistant

      dheemanth-hub
      F
      license
      Not graded
      quality
      D
      maintenance
      Enables querying CSV or Excel data using natural language through MCP tools, running pandas operations on an uploaded dataset.
      -

    • llm-analytics-mcp

      Kirill-FD
      F
      license
      Not graded
      quality
      C
      maintenance
      Enables LLM clients to load, clean, visualize, and analyze tabular datasets, and to generate multi-format analytic reports through natural language instructions.
      -
    • datadeck by postleo

      datadeckofficial

      postleo
      A
      license
      Not graded
      quality
      C
      maintenance
      Enables AI assistants and editors to profile, clean, and analyze messy CSV/Excel files directly through MCP tools such as profile_dataset, clean_dataset, and list_sheets.
      MIT
    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

    B3.3/5.0

    Scored across 6 tools

    Disambiguation4/5

    Each tool has a fairly distinct role: load/unload/list manage dataset lifecycle, describe vs profile inspect content, and query runs arbitrary code. The main ambiguity is between describe_dataset and profile_dataset, which both summarize a dataset, though the descriptions differentiate schema/stats from rule-based findings.

    Naming Consistency5/5

    All tools follow a strict verb_noun snake_case pattern (load_dataset, list_datasets, unload_dataset, describe_dataset, profile_dataset, query_dataset). The naming is predictable and the resource (dataset) is constant across tools.

    Tool Count5/5

    Six tools is well-scoped for an in-memory dataframe analysis server. Each tool covers a distinct lifecycle or inspection action with no obvious redundancy or bloat.

    Completeness4/5

    The surface covers the full load/list/describe/profile/query/unload lifecycle and query_dataset acts as an escape hatch for arbitrary analysis. Minor gaps exist around exporting/saving results or persisting derived datasets, but core workflows are covered.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources