A FastMCP 3.0 server exposing data-processing tools, resources, and prompts over HTTP.
概览
A FastMCP 3.0 server exposing data-processing tools, resources, and prompts over HTTP.
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"
安装
This server does not publish a one-line install command.
Open the repository installation guide 新手？先看 mcpskill.org 教程 →