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FelipeVandevelde

Data Processing MCP Server

by FelipeVandevelde
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Python
Remote

Data Processing MCP Server

A FastMCP 3.0 server exposing data-processing tools, resources, and prompts over HTTP.

Quick Start

1. Install dependencies

pip install -r requirements.txt

2. Run the server

# Simple one-liner (stdio→http)
python server.py

# Or via the FastMCP CLI
fastmcp run server.py:mcp --transport http --port 8000

The server starts at http://localhost:8000/mcp

Related MCP server: CSV Editor

Tools

CSV

Tool

Description

parse_csv

Parse CSV text → list of dicts

summarise_csv

Descriptive statistics for every numeric column

filter_csv_rows

Return rows where column == value

csv_to_json

Convert CSV → JSON array string

JSON

Tool

Description

flatten_json

Flatten nested JSON with dot-notation keys

json_to_csv

Convert a JSON array of objects → CSV

extract_json_keys

List every unique key path in a JSON document

Text

Tool

Description

word_frequency

Top-N word counts in plain text

text_statistics

Characters, words, sentences, paragraphs

find_and_replace

Find & replace with an optional case-insensitive mode

Numeric

Tool

Description

compute_stats

Min, max, mean, median, stdev, variance for a list of numbers

Resources

URI

Description

info://server

Server metadata and capability map

examples://csv

Ready-to-use sample CSV string

examples://json

Ready-to-use sample nested JSON

Prompts

Name

Description

analyse_dataset

Full end-to-end analysis workflow for any dataset

clean_and_convert

Data cleaning + format conversion workflow

Endpoints

Path

Method

Description

/mcp

POST/GET

MCP protocol (StreamableHTTP)

/health

GET

Health check (always unauthenticated)

Production (Uvicorn + multiple workers)

# stateless_http=True is required for multi-worker setups
FASTMCP_STATELESS_HTTP=true uvicorn server:mcp.http_app() \
  --host 0.0.0.0 --port 8000 --workers 4

Or create app.py:

from server import mcp
app = mcp.http_app(stateless_http=True)   # for multi-worker deployments

Then:

uvicorn app:app --host 0.0.0.0 --port 8000 --workers 4

Connect from a client

import asyncio
from fastmcp import Client

client = Client("http://localhost:8000/mcp")

async def main():
    async with client:
        result = await client.call_tool("summarise_csv", {
            "csv_text": "name,score\nAlice,88\nBob,72\nCarol,95"
        })
        print(result)

asyncio.run(main())

Install into Claude Desktop

fastmcp install server.py:mcp --name "Data Processing Server"

This server cannot be deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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