How do I use Excel 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., "@Excel MCP Server Add 'John' to cell B3" 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.

Excel MCP Server

by labeebkm

Overview Schema Related Servers Score Discussions

Python

Local

Excel MCP Agent

An AI-powered Excel automation project built using FastAPI, Model Context Protocol (MCP), Groq LLM, HTTPX, JWT Authentication, and OpenPyXL.

The project enables users to manipulate Excel spreadsheets through natural language by combining an AI agent, an MCP server, and a secure REST API.

Architecture

                  Natural Language Request
                           │
                           ▼
                    Groq LLM Agent
                           │
                           ▼
                     MCP Client
                           │
                           ▼
                     MCP Server
                           │
                           ▼
                HTTPX API Client (JWT)
                           │
                           ▼
                 FastAPI REST API
                           │
                           ▼
                   Excel Workbook

Related MCP server: @node2flow/google-sheets-mcp

Features

Secure REST API using JWT Authentication
AI-powered Excel automation
MCP Server exposing Excel tools
Natural language interaction through Groq LLM
CRUD operations on Excel spreadsheets
Swagger UI documentation
Modular project structure
Automatic JWT handling inside the MCP server

Project Structure

excel_project/
│
├── excel_api/
│   ├── auth.py
│   ├── main.py
│   ├── models.py
│   ├── services.py
│   └── files/
│       └── data.xlsx
│
├── excel_mcp_server/
│   ├── api_client.py
│   ├── config.py
│   └── mcp_server.py
│
├── excel_mcp_client/
│   ├── agent.py
│   ├── client.py
│   ├── llm_config.py
│   ├── main.py
│   └── prompts.py
│
├── requirements.txt
└── README.md

Technologies Used

Python 3
FastAPI
MCP (Model Context Protocol)
Groq API
HTTPX
OpenPyXL
PyJWT
Pydantic
Uvicorn

Excel API Endpoints

Authentication

Method	Endpoint	Description
POST	`/auth/login`	Authenticate user and obtain JWT token
GET	`/auth/me`	Retrieve current authenticated user

Excel Operations

Method	Endpoint	Description
POST	`/addData`	Insert data into a single Excel cell
POST	`/addDataRange`	Insert data into multiple Excel cells
DELETE	`/deleteCellData`	Clear data from a single cell
DELETE	`/deleteCellRange`	Clear data from a range of cells

MCP Tools

The MCP server exposes the following tools:

add_data
add_data_range
delete_cell_data
delete_cell_range

These tools internally communicate with the secured FastAPI application.

Authentication Flow

Client
   │
   ▼
POST /auth/login
   │
   ▼
JWT Access Token
   │
   ▼
Authorization: Bearer <token>
   │
   ▼
Protected Excel APIs

The MCP server automatically:

logs in to the FastAPI application
retrieves a JWT token
stores the token
includes the Authorization header in every API request
re-authenticates if required

Installation

Clone the repository.

git clone https://github.com/<your-username>/excel-mcp-agent.git

Navigate to the project.

cd excel-mcp-agent

Create a virtual environment.

python -m venv .venv

Activate the virtual environment.

Windows

.venv\Scripts\activate

Linux/macOS

source .venv/bin/activate

Install dependencies.

pip install -r requirements.txt

Environment Variables

Create a .env file in the project root.

GROQ_API_KEY=your_groq_api_key

JWT_SECRET_KEY=your_secret_key
JWT_ALGORITHM=HS256
JWT_ACCESS_TOKEN_EXPIRE_MINUTES=30

API_BASE_URL=http://127.0.0.1:8000
API_USERNAME=admin
API_PASSWORD=admin123

Running the FastAPI Server

uvicorn excel_api.main:app --reload

Swagger UI

http://127.0.0.1:8000/docs

Running the MCP Server

python -m excel_mcp_server.mcp_server

mcp dev excel_mcp_server/mcp_server.py:mcp

Running the AI Agent

python -m excel_mcp_client.main

Example prompts:

Add "John" to row 3 column 2

Insert "Apple", "Banana", "Orange" into A1:A3

Delete data from C5

Clear the range B2:D5

Example Workflow

User
   │
   ▼
Natural Language Prompt
   │
   ▼
Groq LLM
   │
   ▼
MCP Client
   │
   ▼
MCP Server
   │
   ▼
FastAPI API
   │
   ▼
Excel Workbook Updated

Future Improvements

Multiple worksheets support
Read/search Excel data
Update existing cell values
File upload support
User management with database
Refresh token implementation
Role-based authorization
Docker support

License

This project is intended for learning purposes.

This server cannot be installed

license - not found

quality - not tested

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

Resources

GitHub Repository

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