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.
CSV Analyzer MCP Server
@juanmaalt
Basic information
About CSV Analyzer MCP Server
No overview available yet
CSV Analyzer MCP Server config
No standard config provided
This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.
RepositoryCSV Analyzer MCP Server tools
Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
Overview
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.
Frequently asked questions
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.
More Data & Analytics MCP servers
Табуля
Табуля — таблица с ИИ, Python и SQL. MCP-сервер даёт Claude, ChatGPT, Cursor и другим агентам доступ к вашим таблицам: чтение и запись, формулы, Python и SQL, оформление и импорт. Tabulya is a spreadsheet with AI, Pytho
Tracetify — Competitor Ads, Growth Traces & Search Console
tracetify17 tools for marketing and SEO research inside Claude Code, Cursor, Codex or ChatGPT: the ads any domain runs on Meta and Google, how a product grew from 12 public sources with every claim linked, your own Search Console
1
LandLens One
Verified.RealEstateAsk your AI about any land in Tamil Nadu. 100+ checks over the state's government registers: who owns a survey number, guideline value, encumbrance (EC), FMB sketch, coastal and zoning checks, road width, stamp duty and
Hologrow
Hologrow IncHologrow is the e-commerce data layer for any AI
CitySignal AI
CitySignal AILive road, transit, weather, power and water signals for 13 US cities, deduplicated and kept current with a week of memory, plus road camera pictures and upcoming events. City tools need no sign-in. Connect a CitySignal
Comments