Skip to main content
Glama
sindhug

MCP DataFrame QA

by sindhug
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Related Servers

Alternatives to MCP DataFrame QA

No user-submitted related servers found.

    Related Servers

    • MCP Data Analysis Agent

      SomanGaurav
      F
      license
      Not graded
      quality
      C
      maintenance
      An MCP server that answers natural-language questions over CSV, Excel, and SQL data by providing deterministic tools for loading, profiling, querying, cleaning, statistical analysis, visualization, and reporting. It enables LLMs to plan and interpret while all computation is done exactly through MCP tools.
      -

    • text-to-sql-mcp

      HamzaOuadid
      A
      license
      Not graded
      quality
      C
      maintenance
      MCP server that translates natural-language questions into SQL, validates every query structurally, and executes approved read-only queries against a SQLite database, returning results and rejections.
      MIT

    • MCP Mix Server

      mlstudios-ai
      F
      license
      A
      quality
      Not graded
      maintenance
      A tutorial implementation MCP server that enables analysis of CSV and Parquet files. Allows users to summarize data and query file information through natural language interactions.
      2
      -

    • ontario-data-mcp

      sprine
      A
      license
      A
      quality
      D
      maintenance
      An MCP server for discovering, downloading, querying, and analyzing datasets from Ontario's open data portals, allowing natural language questions and high-performance analytics via DuckDB.
      23
      1
      MIT

    • MSSQL-MCP

      esasiyun17
      A
      license
      A
      quality
      A
      maintenance
      Security-first, read-only MCP server for Microsoft SQL Server, enabling safe natural-language querying of databases.
      5
      4 npm
      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 3 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose: executing a plan, previewing data, and answering natural language queries. There is no overlap or ambiguity.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern with snake_case, starting with a verb and clearly indicating the action.

    Tool Count4/5

    With only 3 tools, the server is somewhat minimal but still reasonable for focused dataframe QA. The count is on the lower end but not inappropriate.

    Completeness3/5

    The tools cover execution of analysis plans, preview, and query, but lack basic data exploration features like listing columns or summary statistics, which are notable gaps for a QA server.

    Maintenance

    ActivitySlowing
    ResponsivenessNo issues

    Resources