How do I use Data Processing MCP Server?

Q: How do I use Data Processing MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type `@` followed by the MCP server name and your instructions, e.g., "`@Data Processing MCP Server` Summarise this CSV: name,score Alice,88 Bob,72" That's it! The server will respond to your query, and you can continue using it as needed. Here is a [step-by-step guide with screenshots](https://glama.ai/blog/2025-07-08-how-to-install-and-use-mcp-servers).

        1. Click on "Install Server".
        2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
        3. In the chat, type `@` followed by the MCP server name and your instructions, e.g., "`@Data Processing MCP Server` Summarise this CSV: name,score

Alice,88 Bob,72"

        That's it! The server will respond to your query, and you can continue using it as needed.
        
        Here is a [step-by-step guide with screenshots](https://glama.ai/blog/2025-07-08-how-to-install-and-use-mcp-servers).

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

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 installed

F

license - not found

-

quality - not tested

C

maintenance

How are these scores calculated?

Resources

GitHub Repository

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Data Processing MCP Server

Data Processing MCP Server

Quick Start

1. Install dependencies

2. Run the server

Tools

CSV

JSON

Text

Numeric

Resources

Prompts

Endpoints

Production (Uvicorn + multiple workers)

Connect from a client

Install into Claude Desktop

Resources

Looking for Admin?

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