Data Processing MCP Server

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

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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

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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

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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

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Prompts

Name	Description
analyse_dataset	Full end-to-end analysis workflow for any dataset
clean_and_convert	Data cleaning + format conversion workflow

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Endpoints

Path	Method	Description
/mcp	POST/GET	MCP protocol (StreamableHTTP)
/health	GET	Health check (always unauthenticated)

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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

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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())

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Install into Claude Desktop

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