Skip to main content
Glama
devantage

mcp-pandas

by devantage
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

Related Servers

Alternatives to mcp-pandas

No user-submitted related servers found.

    Related Servers

    • ChatBI MCP Server

      Steven-Luo
      F
      license
      Not graded
      quality
      D
      maintenance
      Enables AI-powered business intelligence and data analysis using pandas and LLM code generation. Supports automated data processing, statistical analysis, and visualization creation through natural language interactions.
      15
      -

    • CSV MCP Assistant

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

    • CSV Analytics MCP Server

      CSOAI-ORG
      A
      license
      Not graded
      quality
      B
      maintenance
      A spreadsheet and CSV analysis toolkit for AI agents that enables loading CSV files, filtering and querying data, computing statistics, creating aggregations, building pivot tables, and exporting chart-ready data using pandas.
      6 npm
      52 PyPI
      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

    A4.3/5.0

    Scored across 4 tools

    Disambiguation5/5

    Each tool has a distinct purpose: generating charts, profiling metadata, examining column distributions, and executing arbitrary pandas code. There is no overlap or ambiguity.

    Naming Consistency4/5

    All tool names use snake_case with a verb_noun pattern, but the verbs vary (generate, read, interpret, run). This is consistent within its own style, though not perfectly uniform.

    Tool Count4/5

    Four tools is a minimal but focused set for pandas operations. It covers key actions without being overly sparse or excessive for the domain.

    Completeness4/5

    Core operations like metadata profiling, column analysis, code execution, and visualization are covered. Minor gaps exist (e.g., no direct data subsetting tool) but arbitrary code fills most needs.

    Maintenance

    ActivityStale
    ResponsivenessNo issues

    Resources