2026-01-18

MCP Service Integration - Excel to JSON for AI and Automation Workflows

Welcome to part 9 of our Excel to JSON series! We’ve covered the API for programmatic access, and today we’re exploring MCP Service - an advanced option for developers working with AI tools and automation platforms.

excel-to-json-toolkit

What is MCP?

MCP (Model Context Protocol) is an open protocol that enables AI assistants and automation tools to interact with external services and data sources. The Excel to JSON MCP Service allows AI models and automation platforms to convert Excel to JSON format seamlessly within their workflows.

Why Use MCP Service?

The MCP Service is ideal for:

AI Assistants: Enable Claude, GPT, and other AI models to convert Excel to JSON
Automation Platforms: Integrate with n8n, Make.com, and other automation tools
Custom Workflows: Build automated data processing pipelines
Developer Tools: Create custom integrations with MCP-compatible platforms
No-Code/Low-Code Solutions: Enable non-technical users to automate conversions

Getting Started with MCP Service

Installation

The Excel to JSON MCP Service is available as an npm package:

1	npm install -g @wtsolutions/excel-to-json-mcp

GitHub Repository

Full documentation and source code are available at:
https://github.com/he-yang/excel-to-json-mcp

Requirements

Node.js 14 or higher
npm or yarn package manager
Valid Pro Code for advanced features

MCP Service Configuration

Basic Setup

After installation, you’ll need to configure MCP Service for your specific use case. The configuration typically includes:

Server Connection Details
- Host and port settings
- Authentication credentials (if required)
Pro Code Configuration
- Your email address as Pro Code
- Enables Pro features like custom delimiters and format options
Conversion Settings
- Default conversion mode (flat/nested)
- Default delimiter choice
- Default header option (row/column)

Example Configuration

{
  "mcpServers": {
    "excel-to-json": {
      "command": "npx",
      "args": [
        "-y",
        "@wtsolutions/excel-to-json-mcp"
      ],
      "env": {
        "PRO_CODE": "[email protected]"
      }
    }
  }
}

Using MCP with AI Assistants

Claude Desktop Integration

Claude Desktop supports MCP servers natively. Here’s how to integrate Excel to JSON:

Step 1: Configure Claude Desktop

Open Claude Desktop settings
Navigate to MCP Servers section
Add Excel to JSON MCP Server configuration
Enter your Pro Code if you have one

Step 2: Use in Conversation

Once configured, you can ask Claude to convert Excel to JSON:

User: I have this Excel data about sales:

| Product | Price | Quantity |
|---------|-------|----------|
| Laptop  | 999.99 | 50       |
| Mouse   | 29.99  | 200      |

Can you convert this to JSON format?

Claude: I'll use the Excel to JSON MCP server to convert this data for you.

[Processing...]

Done! I've converted your Excel data to JSON format. Here's the result:

```json
[
  {
    "Product": "Laptop",
    "Price": 999.99,
    "Quantity": 50
  },
  {
    "Product": "Mouse",
    "Price": 29.99,
    "Quantity": 200
  }
]


### Other AI Platforms

The MCP Service can be integrated with other AI platforms that support MCP:

- **Claude API**: Direct integration in your applications
- **Custom AI Solutions**: Build your own AI assistants with MCP support
- **Enterprise AI Platforms**: Integrate with enterprise AI systems

## Automation Workflows

### n8n Integration

n8n is a powerful automation platform that supports MCP. Here's how to use Excel to JSON in n8n workflows:

#### Workflow Example: Excel to JSON to Database

1. **Webhook Trigger**: Trigger on schedule or external event
2. **Excel Data Node**: Read Excel file from local storage or HTTP
3. **MCP Server Node**: Convert Excel to JSON using MCP Service
4. **Database Node**: Save JSON data to database
5. **Notification Node**: Send success notification

#### Configuration Steps

1. Add MCP Server node to your n8n workflow
2. Select "excel-to-json" as the server
3. Configure input data (Excel file or data)
4. Set conversion options (flat/nested, delimiter, etc.)
5. Connect to output nodes (database, API, etc.)

### Make.com Integration

Make.com (formerly Integromat) also supports MCP servers:

#### Scenario: Automated Report Generation

1. **Google Sheets**: Read Excel data from Google Sheets
2. **MCP Server**: Convert to JSON using Excel to JSON MCP Service
3. **Format Data**: Apply formatting and calculations
4. **Email**: Send formatted report as email
5. **Slack**: Notify team that report is ready

## Custom MCP Client Implementation

For developers who want to build custom MCP clients:

### Python MCP Client

```python
import asyncio
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

async def convert_excel_to_json(excel_data):
    # Create server parameters
    server_params = StdioServerParameters(
        command="npx",
        args=["-y", "@wtsolutions/excel-to-json-mcp"],
        env={"PRO_CODE": "[email protected]"}
    )
    
    # Connect to MCP server
    async with stdio_client(server_params) as (read, write):
        async with ClientSession(read, write) as session:
            # Initialize session
            await session.initialize()
            
            # Call conversion tool
            result = await session.call_tool(
                "convert_excel_to_json",
                arguments={
                    "data": excel_data,
                    "options": {
                        "jsonMode": "nested",
                        "delimiter": ".",
                        "header": "row"
                    }
                }
            )
            
            return result

# Usage
import json

excel_data = "Name\tAge\tCompany\nJohn Doe\t25\tWTSolutions\nJane Smith\t30\tMicrosoft"

result = asyncio.run(convert_excel_to_json(excel_data))
print(result)

JavaScript MCP Client

const { Client } = require('@modelcontextprotocol/sdk/client/index.js');
const { StdioClientTransport } = require('@modelcontextprotocol/sdk/client/stdio.js');

async function convertExcelToJson(excelData) {
  // Create transport
  const transport = new StdioClientTransport({
    command: 'npx',
    args: ['-y', '@wtsolutions/excel-to-json-mcp'],
    env: {
      PRO_CODE: '[email protected]'
    }
  });
  
  // Create client
  const client = new Client({
    name: 'excel-to-json-client',
    version: '1.0.0'
  }, {
    capabilities: {}
  });
  
  try {
    // Connect to server
    await client.connect(transport);
    
    // Call conversion tool
    const result = await client.callTool({
      name: 'convert_excel_to_json',
      arguments: {
        data: excelData,
        options: {
          jsonMode: 'nested',
          delimiter: '.',
          header: 'row'
        }
      }
    });
    
    return result;
  } finally {
    await client.close();
  }
}

// Usage
const excelData = 'Name\tAge\tCompany\nJohn Doe\t25\tWTSolutions\nJane Smith\t30\tMicrosoft';

convertExcelToJson(excelData)
  .then(result => console.log(result))
  .catch(error => console.error(error));

Advanced MCP Features

Custom Tool Definitions

The Excel to JSON MCP Service exposes tools that can be called by MCP clients:

Tool: convert_excel_to_json

Description: Converts Excel data to JSON format

Parameters:

data (string, required): Excel data to convert
options (object, optional): Conversion options
- jsonMode (string): “flat” or “nested”
- header (string): “row” or “column”
- delimiter (string): “.”, “_”, “__”, or “/“
- emptyCell (string): “emptyString”, “null”, or “exclude”
- booleanFormat (string): “trueFalse”, “10”, or “string”
- jsonFormat (string): “arrayOfObject” or “2DArray”
- singleObjectFormat (string): “array” or “object”

Returns: JSON data or conversion result

Error Handling

The MCP Service provides detailed error information:

{
  "success": false,
  "error": {
    "code": "INVALID_DATA",
    "message": "The provided Excel data is not valid",
    "details": "At least 2 rows are required"
  }
}

Logging and Debugging

Enable detailed logging for troubleshooting:

1	DEBUG=mcp:* npx -y @wtsolutions/excel-to-json-mcp

Use Cases

Use Case 1: AI-Powered Data Analysis

Scenario: Use Claude to analyze Excel data and create JSON for APIs

Workflow:

User provides Excel data to Claude
Claude uses MCP Service to convert to JSON
Claude analyzes the JSON data
Claude provides insights and recommendations
User exports JSON for API integration

Benefits:

Seamless AI integration
No manual conversion steps
Faster data-to-insights pipeline
Better data understanding

Use Case 2: Automated Data Pipeline

Scenario: Daily automated conversion of Excel reports to JSON for downstream systems

Workflow:

Cron job triggers at scheduled time
Script reads Excel file from network share
MCP Service converts to JSON
JSON file is uploaded to cloud storage
Downstream systems process JSON data
Team receives notification

Benefits:

Fully automated process
No manual intervention required
Reliable, scheduled execution
Integration with existing systems

Use Case 3: Real-Time Data Processing

Scenario: Convert Excel data in real-time as it arrives from multiple sources

Workflow:

Webhook receives Excel data
MCP Service immediately converts to JSON
JSON data is processed by downstream systems
Results are sent back to originator

Benefits:

Near-instant conversion
Scalable for high-volume data
Integrates with existing systems
Real-time data flow

Use Case 4: Multi-Platform Data Aggregation

Scenario: Aggregate Excel data from multiple sources and convert to unified JSON format

Workflow:

Fetch Excel data from Source A, B, C
Use MCP Service to convert each to JSON
Merge JSON data from all sources
Apply business logic and transformations
Distribute unified JSON to stakeholders

Benefits:

Centralized data processing
Consistent output format
Easy to maintain and update
Better data governance

Best Practices

1. Security Considerations

Protect Pro Codes: Don’t expose Pro Codes in client-side code
Validate Input: Always validate Excel data before conversion
Use HTTPS: Ensure secure communication channels
Limit Access: Restrict MCP Service access as needed

2. Performance Optimization

Batch Processing: Convert multiple Excel objects together when possible
Caching: Cache conversion results for repeated data
Async Operations: Use asynchronous processing for better performance
Resource Management: Monitor and manage server resources

3. Error Handling

Graceful Degradation: Handle errors without crashing workflows
Retry Logic: Implement retry mechanisms for transient failures
Logging: Log errors for debugging and monitoring
User Feedback: Provide clear error messages to users

4. Testing

Unit Tests: Test MCP client implementations thoroughly
Integration Tests: Test with actual MCP Service
Edge Cases: Test with various Excel structures
Load Testing: Test performance under high load

Troubleshooting

Common Issues

Issue 1: Connection Failed

Cause: MCP Service not running or incorrect configuration
Solution: Verify server is running and check configuration

Issue 2: Invalid Pro Code

Cause: Pro Code is incorrect or expired
Solution: Verify Pro Code and ensure subscription is active

Issue 3: Conversion Timeout

Cause: Large Excel data or server overload
Solution: Split data into smaller chunks or retry later

Issue 4: Invalid Excel Format

Cause: Input Excel data is malformed
Solution: Validate Excel data before sending to MCP Service

Debug Mode

Enable debug mode for detailed troubleshooting:

1	DEBUG=excel-to-json:* npx -y @wtsolutions/excel-to-json-mcp

MCP vs API: Which to Choose?

Feature	MCP Service	API
AI Integration	✅ Native	❌ Requires wrapper
Automation Platforms	✅ Native support	❌ Requires HTTP calls
Custom Applications	✅ Standard protocol	✅ Simple HTTP
Learning Curve	⚠️ Moderate	✅ Simple
Flexibility	✅ High	✅ High
Real-time Capabilities	✅ Excellent	✅ Good

Choose MCP Service when:

Working with AI assistants
Using automation platforms with MCP support
Building custom AI-powered solutions
Need standardized protocol integration

Choose API when:

Building simple web applications
Need straightforward HTTP integration
Working with traditional development stacks
Prefer direct control over requests

Next Steps

Now that you understand MCP Service integration, you have a complete picture of all Excel to JSON tools available. In our final post, we’ll explore real-world use cases and practical examples that demonstrate how organizations are using Excel to JSON to solve actual business problems.

Ready to integrate with MCP? Check out the GitHub repository for detailed documentation and examples!

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MCP Service Integration - Excel to JSON for AI and Automation Workflows

What is MCP?

Why Use MCP Service?

Getting Started with MCP Service

Installation

GitHub Repository

Requirements

MCP Service Configuration

Basic Setup

Example Configuration

Using MCP with AI Assistants

Claude Desktop Integration

Step 1: Configure Claude Desktop

Step 2: Use in Conversation

JavaScript MCP Client

Advanced MCP Features

Custom Tool Definitions

Tool: convert_excel_to_json

Error Handling

Logging and Debugging

Use Cases

Use Case 1: AI-Powered Data Analysis

Use Case 2: Automated Data Pipeline

Use Case 3: Real-Time Data Processing

Use Case 4: Multi-Platform Data Aggregation

Best Practices

1. Security Considerations

2. Performance Optimization

3. Error Handling

4. Testing

Troubleshooting

Common Issues

Debug Mode

MCP vs API: Which to Choose?

Next Steps