alt text

uv init mcp-server-demo

cd mcp-server-demo

uv add "mcp[cli]" uv pip install mcp-python pandas matplotlib openpyxl fastapi uvicorn pillow uv run mcp dev server.py

uv run mcp install server.py

Excel MCP Server

This project implements a Model Context Protocol (MCP) server that provides tools and resources for working with Excel data. The server offers functionality for reading, updating, filtering, and visualizing Excel data through a simple API.

Features

Excel Operations: Read from and write to Excel files
Data Filtering: Filter and search data based on various criteria
Data Analysis: Generate statistical summaries and pivot tables
Data Visualization: Create various charts and visualizations
Update Operations: Update cells, add rows, delete rows
Anomaly Detection: Find outliers in numeric data
Chart Recommendations: Automatically suggest appropriate visualizations

Related MCP server: Excel MCP Server

Installation

Install the required dependencies:

pip install mcp-python pandas matplotlib openpyxl fastapi uvicorn pillow

Clone this repository or download the files:
- server.py - The MCP server implementation
- client.py - Demo client to showcase the features

Usage

Starting the Server

Run the server:

python server.py

The server will start at http://localhost:8000 by default.

Using the Client Demo

The client demo script showcases various operations you can perform with the MCP server:

python client.py

This will:

Create a sample Excel file (if it doesn't exist)
Demonstrate basic Excel operations
Show data filtering capabilities
Generate visualizations
Perform data updates
Detect anomalies in the data

Using the API Directly

You can use the MCP server's API directly:

Tools API

Call tools using POST requests to /tools/{tool_name}:

import requests # Example: Read Excel file response = requests.post( "http://localhost:8000/tools/read_excel", json={"filename": "example.xlsx", "sheet_name": "Sheet1"} ) data = response.json()

Resources API

Access resources using GET or POST requests to /resources/{resource_path}:

# Example: Get sheet list response = requests.get("http://localhost:8000/resources/excel://example.xlsx/sheets") sheets = response.json()

Available Tools

Excel Operations

read_excel - Read data from an Excel file
write_excel - Write data to an Excel file
get_excel_sheets - Get the list of sheets in an Excel file

Data Filtering

filter_data - Filter Excel data based on conditions
search_data - Search for a term in Excel data

Data Analysis

summarize_data - Get statistical summary of Excel data
create_pivot_table - Create a pivot table from Excel data

Data Visualization

visualize_chart - Create various chart types (bar, line, scatter, pie, hist)
recommend_charts - Get chart recommendations based on data structure

Update Operations

update_cell - Update a specific cell in an Excel file
add_row - Add a new row to an Excel file
delete_rows - Delete rows from an Excel file based on filters

Additional Features

detect_anomalies - Find anomalies in numeric data using Z-scores
export_to_csv - Export Excel data to CSV format

Available Resources

excel://{filename}/sheets - Get list of sheets in an Excel file
excel://{filename}/sheet/{sheet_name} - Get data from a specific sheet
excel://{filename}/sheet/{sheet_name}/summary - Get summary of sheet data
excel://{filename}/sheet/{sheet_name}/filter - Filter data in a sheet

Example: Creating a Custom Client

You can create your own client to interact with the MCP server:

import requests def call_tool(tool_name, params): """Call an MCP tool""" url = f"http://localhost:8000/tools/{tool_name}" response = requests.post(url, json=params) return response.json() # Example: Get chart visualization chart_result = call_tool("visualize_chart", { "filename": "sales_data.xlsx", "sheet_name": "Sheet1", "chart_type": "bar", "x_column": "Month", "y_columns": ["Revenue"], "title": "Monthly Revenue" }) # Save chart image if chart_result.get("success", False) and "image" in chart_result: import base64 img_data = base64.b64decode(chart_result["image"]) with open("revenue_chart.png", "wb") as f: f.write(img_data)