CSV Analyzer MCP Server

What is CSV Analyzer MCP Server?

CSV Analyzer MCP Server is a lightweight, modular tool that analyzes CSV datasets and produces structured summaries for consumption by language models like ChatGPT or Claude. It is designed for developers who need to prepare CSV data for LLM processing.

How to use CSV Analyzer MCP Server?

Clone the repository, install dependencies with uv pip install -r requirements.txt, then invoke the analyze_csv function with parameters: csv_content (raw CSV string), delimiter (default ,), remove_duplicates (default True), remove_non_valid_data (default True), and output_format (json or markdown, default json).

Key features of CSV Analyzer MCP Server

• Processes raw CSV content as a string.
• Removes duplicate rows and rows with missing values.
• Returns results in JSON or Markdown format.
• Supports customizable CSV delimiters.
• Lightweight and easy to integrate.

Use cases of CSV Analyzer MCP Server

• Preprocessing CSV data before feeding it to an LLM.
• Cleaning datasets by removing duplicates and incomplete rows.
• Converting CSV summaries into structured JSON for downstream tools.
• Preparing CSV content in Markdown for human-readable LLM prompts.

FAQ from CSV Analyzer MCP Server

What dependencies are required?

You need Python and uv (or pip) to install the dependencies listed in requirements.txt. The server itself is written in Python.

How do I clean the CSV data?

Set remove_duplicates=True to drop duplicate rows, and remove_non_valid_data=True to drop rows with missing values. Both are True by default.

What output formats are supported?

The tool returns results in JSON (default) or Markdown, controlled by the output_format parameter.

Can I use a different delimiter?

Yes, pass the desired delimiter as the delimiter parameter; the default is a comma (,).

How is the CSV data provided to the server?

Data is passed as a raw string via the csv_content parameter – no file path or upload is used.