Dataset Explorer

io.github.khanarmaghanrasheed-18v0.2.1更新於 Oct 8, 2026

Help your AI explore local data using summaries, Pearson correlation, eta squared, and Cramer's V.

已驗證STDIO僅桌面Files & StorageData & Analytics

概覽

AI 產生的概覽

讓助理讀取本機的 CSV、Excel、JSON 或 Parquet 檔案，並計算摘要、缺失值、重複列、離群值與欄位間關聯。

功能
Dataset Explorer 在本機執行，載入你指定的資料檔案，回傳實際計算出的統計結果而非猜測。工具包括 get_dataset_overview、dataset_shape、dataset_statistical_summary、inspect_Column、analyze_target、duplicate_finder、analyze_missing_values、find_correlations、detect_outliers 與 screen_target_relationships。它會依所比較欄位的型別選擇 Pearson 相關係數、eta 平方或 Cramer's V。另外也提供 dataset://guide 資源與 explore_dataset 提示。
適用情境
當你希望助理檢查自己電腦上的資料檔案時使用：查看欄位型別與缺失值、找出重複列、發現離群值，或了解哪些特徵與目標欄位相關。它著重探索性分析，不用於建模或因果推論。
執行需求
本機 Python 3.10 以上版本；可從 PyPI 安裝 dataset-explorer-mcp，或以 uvx 免安裝執行。需要支援本機 stdio 伺服器的 MCP 用戶端，例如 Claude Desktop、VS Code with Copilot 或 Cursor。資料檔案必須在同一台電腦上可讀取，大型檔案需要足夠記憶體，因為每個工具都會把資料集載入記憶體。
安裝前請注意
伺服器會讀取你指定的資料檔案，且不修改原始檔案，但不限制可讀取的路徑。結果可能依用戶端設定傳送給助理的 AI 供應商，因此除非可以接受，否則不要指向敏感檔案。統計結果只反映關聯，不能證明因果關係。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Dataset Explorer，將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Desktop only，透過 STDIO。 STDIO 服務會啟動本機處理程序，因此需要 SourceWeft 桌面主機。

其他 MCP 客戶端

參照 儲存庫 中的啟動說明。

README

Dataset Explorer MCP

Dataset Explorer helps your AI assistant understand data files saved on your computer. Ask it to summarize a dataset, check missing values, find repeated rows, or spot unusual patterns. It calculates answers from your file and leaves the original unchanged.

It works with CSV, TSV, Excel, JSON, and Parquet files. Your assistant starts this small local server when needed. You don't need a web app, a hosting account, or a Gemini API key. Your assistant may send the results to its AI provider according to that client's settings.

The connection uses MCP over stdio, which simply means your assistant talks directly to the server running on your computer.

How it finds relationships

The server calculates statistics from your dataset rather than guessing them:

  • Pearson correlation checks how two numeric columns move together.
  • Eta squared compares numeric values across groups, such as scores across categories.
  • Cramer's V measures the relationship between two category columns.

It chooses the method based on the types of columns being compared. These statistics show associations; they don't prove that one feature causes another.

Install

Available on PyPI.

You need Python 3.10 or newer.

sh
python -m pip install dataset-explorer-mcp

The command to start the server is:

sh
dataset-explorer-mcp

Your MCP client normally runs this command for you. If you run it in a terminal, it waits quietly for messages from a client. That is expected.

If you use uv, you can run it without a separate installation:

sh
uvx dataset-explorer-mcp

Connect your assistant

Use an MCP client that supports local stdio servers, such as Claude Desktop, VS Code with Copilot, or Cursor. The settings file differs by client.

For Claude Desktop or Cursor, add this to your MCP configuration:

json
{  "mcpServers": {    "dataset-explorer": {      "command": "uvx",      "args": ["dataset-explorer-mcp"]    }  }}

For VS Code, use .vscode/mcp.json:

json
{  "servers": {    "dataset-explorer": {      "type": "stdio",      "command": "uvx",      "args": ["dataset-explorer-mcp"]    }  }}

If you installed with pip, use "command": "dataset-explorer-mcp" and "args": [] instead. An absolute path to the executable also works. Reload your client after changing its configuration.

Use a local file

Give your assistant the full path to your dataset. For example:

Explore C:/Users/YourName/Downloads/customers.csv. Check missing values and duplicates.

Summarize /home/yourname/data/sales.xlsx and inspect the revenue column.

In /Users/yourname/data/results.parquet, which features are associated with the target column score?

All tools take a path. A direct tool call looks like:

json
{"path": "C:/Users/YourName/Downloads/customers.csv"}

Use forward slashes in Windows paths, or double backslashes when writing JSON. The file must be available on the computer where the server runs.

Supported files and tools

Supported files: CSV, TSV, Excel (.xlsx, .xls), JSON, and Parquet. Excel reads the first worksheet. JSON must contain tabular data that Pandas can read.

ToolWhat it does
get_dataset_overviewLists columns, types, and missing-value counts
dataset_shapeCounts rows and columns
dataset_statistical_summaryCalculates numeric means and medians
inspect_ColumnSummarizes one column; also takes col_name
analyze_targetInspects a target column; also takes target_name
duplicate_finderFinds repeated rows
analyze_missing_valuesReports missing data
find_correlationsFinds related numeric columns; optional threshold defaults to 0.8
detect_outliersFinds unusual numeric values
screen_target_relationshipsCompares features with a target; also takes target

The server also offers the dataset://guide resource and an explore_dataset prompt. These results help you explore data; they don't prove causes or train a model. Large files need enough RAM because each tool loads the dataset into memory.

Troubleshooting

  • Command not found: use the full path to dataset-explorer-mcp, or install uv and use the uvx configuration above.
  • File not found: use an absolute path and check that the server can read it.
  • No tools appear: check your client's server logs and reload its MCP settings.
  • Server seems idle: it is waiting for the MCP client; connect it through your assistant rather than typing questions into the server terminal.
  • Unsupported file: save the data in one of the formats listed above.
  • Missing values in statistics: empty or constant columns may have undefined statistics. Check the overview and missing-value tools first.

Normal server output is reserved for MCP messages. Diagnostics go to stderr, which your client's server logs usually display.

Run from source

sh
git clone https://github.com/khanarmaghanrasheed-18/MCP-Dataset-Explorer.gitcd MCP-Dataset-Explorerpython -m pip install -e ".[dev]"python -m pytest -qpython mcp_server.py

Build the downloadable package with python -m build.

License

MIT. See LICENSE.

來源：README.md，提交 c5b6fbd

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版本歷史

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  1. v0.2.1最新Oct 8, 2026