File Analysis

Provides file I/O, PDF text extraction, CSV analysis, and data visualization via MCP clients.

python

5

GitHub Stars

python

Language

6 months ago

First Indexed

2 months ago

Catalog Refreshed

Documentation & install

Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs

{
  "mcpServers": {
    "huangyz0918-file-analysis-mcp-server": {
      "command": "mcp",
      "args": [
        "dev",
        "file_analysis_server.py"
      ],
      "env": {
        "MCP_FILE_ROOTS": "~/Documents:~/Desktop:~/Downloads"
      }
    }
  }
}

You can run a dedicated File Analysis MCP Server to securely read, write, and analyze files, extract text from PDFs, and visualize CSV data. It provides practical, client-accessible tools for everyday data work, directly from your MCP-enabled client.

How to use

Once you have the server installed, you can interact with it from any MCP client. You can read and write text files, list files in directories, fetch file details, extract PDF text, sample and analyze CSV data, and generate visualizations such as bar, line, scatter, histogram, and boxplots. Use natural language prompts to ask for specific tasks, for example: read a file, analyze a CSV, or create a chart from a column.

How to install

Prerequisites you need before installing the server:

# Install the MCP client tooling and Python dependencies
pip install "mcp[cli]" pandas numpy matplotlib PyPDF2

Optional: customize safe directories for file access

Default directories are ~/Documents and ~/Downloads

To customize, set this environment variable:

export MCP_FILE_ROOTS="~~/Documents:~~/Desktop:~/Downloads"

## Additional setup steps

To install and run the File Analysis MCP Server in a development or testing environment, use the MCP command to install and then start a dev session.

mcp install file_analysis_server.py mcp dev file_analysis_server.py


The dev command starts the MCP Inspector interface for interactive testing.

Available tools

Analyze Data File

Comprehensive data analysis workflow for a CSV file, including statistics, sampling, and visualizations.

Data Cleaning Steps

Identify and fix common data quality issues in a dataset, preparing it for analysis.

Generate Summary Report

Create an executive summary detailing key findings from a dataset.

Exploratory Data Analysis

Detailed EDA workflow to uncover patterns, correlations, and insights in data.