Overview

This MCP (Model Context Protocol) server provides a simple, production-ready service to convert JSON data into CSV format. It accepts either raw JSON payloads or publicly accessible .json URLs, performs robust parsing (including basic flattening of nested structures), and returns a CSV file suitable for spreadsheets, BI tools, or downstream ETL pipelines.

The server is designed for developers who need a fast, reliable transformation endpoint that can be integrated into CI pipelines, microservices, or data ingestion flows. It exposes straightforward HTTP endpoints and supports common configuration options such as delimiter selection, header inclusion, and temporary file handling.

Features

Convert JSON arrays or newline-delimited JSON to CSV
Accept raw JSON POST bodies or a public .json URL to fetch and convert
Options for delimiter (comma, semicolon, tab) and header row control
Basic flattening of nested objects (dot notation)
Return CSV directly in response or provide a downloadable file link
Size and timeout controls to protect against very large inputs
Minimal, RESTful API suitable for automation and scripting

Installation / Configuration

Clone and run the server locally, or run with Docker.

Using Git and Node (example):

git clone https://github.com/he-yang/json-to-excel-mcp.git
cd json-to-excel-mcp
# Install dependencies (example uses npm; project may use yarn)
npm install

# Run in development
npm start

# Or run with environment variables
PORT=8080 TMP_DIR=/tmp/json-to-csv node server.js

Using Docker:

# Build
docker build -t json-to-csv-mcp .

# Run
docker run -d -p 8080:8080 \
  -e PORT=8080 \
  -e TMP_DIR=/tmp \
  --name json_to_csv json-to-csv-mcp

Example .env configuration (adapt to your deployment):

PORT=8080
TMP_DIR=/tmp/json_to_csv
MAX_PAYLOAD_MB=10
REQUEST_TIMEOUT_MS=30000
DEFAULT_DELIMITER=,
INCLUDE_HEADERS=true

Available Resources

Source code and issues: https://github.com/he-yang/json-to-excel-mcp
Example JSON files and sample CSV outputs are usually included in the repo under an examples/ directory (check the repo for specifics)
Use the server’s OpenAPI/Swagger (if bundled) or read the README in the repository for endpoint details

API Examples / Usage

Convert raw JSON (POST):

curl -X POST "http://localhost:8080/convert" \
  -H "Content-Type: application/json" \
  -d '[{"id":1,"name":"Alice","meta":{"age":30}}, {"id":2,"name":"Bob","meta":{"age":25}}]'
# Response: CSV text with headers id,name,meta.age

Convert from a public .json URL (GET or POST depending on server implementation):

curl -G "http://localhost:8080/convert" \
  --data-urlencode "url=https://example.com/data/sample.json" \
  --data "delimiter=;" \
  --data "headers=true"

Python example using requests:

import requests
js = [{"id":1,"v":10},{"id":2,"v":20}]
resp = requests.post("http://localhost:8080/convert", json=js)
csv_text = resp.text
print(csv_text)

Request parameters (common):

Parameter	Type	Default	Description
url	string	—	Public URL to fetch JSON from
delimiter	string	“,”	CSV delimiter (e.g., “,”, “;”, “\t”)
headers	boolean	true	Include header row in CSV
flatten	boolean	true	Flatten nested objects using dot notation

Response:

200 OK: CSV content (Content-Type: text/csv or application/octet-stream)
400 Bad Request: malformed JSON or missing data
502/504: upstream fetch failure or timeout when downloading a URL

Use Cases

Export API results to CSV for business reporting or Excel analysis
Transform dataset snapshots hosted as public JSON into CSV for BI ingestion
Lightweight ETL step: fetch public .json URL, convert, and push CSV to storage
Integration in serverless workflows where a small HTTP call returns a CSV file

Example concrete scenario:

A monitoring system exposes daily metrics at https://data.example.com/metrics.json. Call the MCP server with the URL, receive a flattened CSV with headers, and pipe that into a data warehouse loader.

Notes and Limits

The server is intended for public URLs and provided data; it does not fetch private/authenticated endpoints unless you configure credentials.
For very large JSON files, use streaming-compatible endpoints or increase MAX_PAYLOAD settings cautiously.
Nested arrays inside objects may require pre-processing or custom flattening rules; check the repository for advanced configuration.

For implementation details, refer to the repository at https://github.com/he-yang/json-to-excel-mcp and inspect configuration files and example scripts included there.

JSON to CSV Converter MCP Server