Data Processing MCP Server
A FastMCP server for data processing tasks including CSV, JSON, text analysis, and numeric statistics, enabling users to parse, summarise, filter, convert, and analyze data through MCP tools.
README
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"
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