Using the JSON to Excel API - Programmatic Access for Developers

Welcome to part 8 of our JSON to Excel series! We've covered the user-facing tools: Web App, Excel Add-in, and WPS Add-in, along with Pro features. Today, we're exploring the JSON to Excel API - the perfect solution for developers who need to integrate JSON to Excel functionality into their applications and workflows.

Introduction to the JSON to Excel API

The JSON to Excel API provides a powerful, programmatic way to convert JSON data to CSV format (which can be easily imported into Excel). It's designed for developers who need to:

• Automate JSON to Excel conversions in their applications
• Integrate conversion capabilities into existing workflows
• Process JSON data from web services and APIs
• Build custom solutions around JSON to Excel functionality

API Overview

Endpoint

The JSON to Excel API is accessible via a single endpoint:

POST https://mcp2.wtsolutions.cn/json-to-excel-api

Two Usage Modes

The API offers two distinct usage modes:

1. Standard Mode: Free of charge, with standard conversion rules
2. Pro Mode: Requires valid subscription, with custom conversion rules

Standard API Usage

Request Format

The Standard API accepts POST requests with application/json content type containing one of two parameters:

Parameter	Type	Required	Description
data	string	No	JSON data string to be converted. Must be valid JSON array or object
url	string	No	URL pointing to a JSON file. Either 'data' or 'url' must be provided

Important: Provide either data or url, not both.

Request Examples

Example 1: Converting JSON Data

Request:

{
  "data": "[{\"name\": \"WTSolutions\", \"age\": 18},{\"name\": \"David\", \"age\": 20}]"
}

Response:

{
  "data": "\"name,age\\nWTSolutions,18\\nDavid,20\"",
  "isError": false,
  "msg": "success"
}

Example 2: Converting from URL

Request:

{
  "url": "https://tools.wtsolutions.cn/example.json"
}

Response:

{
  "data": "\"name,age\\nWTSolutions,18\\nDavid,20\"",
  "isError": false,
  "msg": "success"
}

Response Format

The API returns a JSON object with the following structure:

Field	Type	Description
isError	boolean	Indicates if there was an error processing the request
msg	string	'success' or error description
data	string	Converted CSV data string, '' if there was an error

Error Response Example

{
  "isError": true,
  "msg": "Invalid JSON format",
  "data": ""
}

Pro API Usage

Request Format

The Pro API accepts POST requests with application/json content type containing:

Parameter	Type	Required	Description
data	string	No	JSON data string to be converted. Must be valid JSON array or object
url	string	No	URL pointing to a JSON file. Either 'data' or 'url' must be provided
options	object	Yes	Configuration object for customizing the conversion process

Important:

• Provide either data or url, not both
• options is mandatory for Pro mode
• You must have a valid Pro Code to use Pro mode

Options Object

The options object can contain the following properties:

Property	Type	Default	Description
proCode	string	""	Pro Code for custom conversion rules. This is mandatory.
jsonMode	string	"flat"	Format mode: "nested" or "flat"
delimiter	string	"."	Delimiter for nested JSON keys when using jsonMode: "nested". Acceptable: ".", "_", "__", "/"
maxDepth	string	"unlimited"	Maximum depth for nested JSON objects when using jsonMode: "nested". Acceptable: "unlimited", "1" ~ "20"

Pro Request Example

Request:

{
  "data": "[{\"name\":\"John\",\"contact\":{\"email\":\"john@example.com\",\"phone\":\"1234567890\"}},{\"name\":\"Jane\",\"contact\":{\"email\":\"jane@example.com\",\"phone\":\"0987654321\"}}]",
  "options": {
    "proCode": "your-email@example.com",
    "jsonMode": "nested",
    "delimiter": ".",
    "maxDepth": "unlimited"
  }
}

Response:

{
  "isError": false,
  "data": "name,contact.email,contact.phone\nJohn,john@example.com,1234567890\nJane,jane@example.com,0987654321",
  "msg": "success"
}

Implementation Examples

Python Implementation

Standard Mode

import requests
import json

# API endpoint
url = "https://mcp2.wtsolutions.cn/json-to-excel-api"

# Prepare your JSON data
json_data = [
    {"name": "John", "age": 30},
    {"name": "Jane", "age": 25}
]

# Make the request
response = requests.post(
    url,
    json={"data": json.dumps(json_data)},
    headers={"Content-Type": "application/json"}
)

# Process the response
result = response.json()

if not result["isError"]:
    csv_data = result["data"]
    print("CSV Data:", csv_data)
    # Save to file
    with open("output.csv", "w") as f:
        f.write(csv_data)
else:
    print("Error:", result["msg"])

Pro Mode

import requests
import json

# API endpoint
url = "https://mcp2.wtsolutions.cn/json-to-excel-api"

# Prepare your JSON data with nested structure
json_data = [
    {
        "name": "John",
        "contact": {
            "email": "john@example.com",
            "phone": "1234567890"
        }
    },
    {
        "name": "Jane",
        "contact": {
            "email": "jane@example.com",
            "phone": "0987654321"
        }
    }
]

# Make the request with Pro options
response = requests.post(
    url,
    json={
        "data": json.dumps(json_data),
        "options": {
            "proCode": "your-email@example.com",
            "jsonMode": "nested",
            "delimiter": ".",
            "maxDepth": "unlimited"
        }
    },
    headers={"Content-Type": "application/json"}
)

# Process the response
result = response.json()

if not result["isError"]:
    csv_data = result["data"]
    print("CSV Data:", csv_data)
    # Save to file
    with open("output.csv", "w") as f:
        f.write(csv_data)
else:
    print("Error:", result["msg"])

JavaScript/Node.js Implementation

Standard Mode

const axios = require('axios');

// API endpoint
const url = 'https://mcp2.wtsolutions.cn/json-to-excel-api';

// Prepare your JSON data
const jsonData = [
  { name: "John", age: 30 },
  { name: "Jane", age: 25 }
];

// Make the request
axios.post(url, {
  data: JSON.stringify(jsonData)
}, {
  headers: {
    'Content-Type': 'application/json'
  }
})
.then(response => {
  const result = response.data;
  if (!result.isError) {
    console.log('CSV Data:', result.data);
    // Save to file (Node.js)
    const fs = require('fs');
    fs.writeFileSync('output.csv', result.data);
  } else {
    console.log('Error:', result.msg);
  }
})
.catch(error => {
  console.error('Request failed:', error);
});

Pro Mode

const axios = require('axios');

// API endpoint
const url = 'https://mcp2.wtsolutions.cn/json-to-excel-api';

// Prepare your JSON data with nested structure
const jsonData = [
  {
    name: "John",
    contact: {
      email: "john@example.com",
      phone: "1234567890"
    }
  },
  {
    name: "Jane",
    contact: {
      email: "jane@example.com",
      phone: "0987654321"
    }
  }
];

// Make the request with Pro options
axios.post(url, {
  data: JSON.stringify(jsonData),
  options: {
    proCode: 'your-email@example.com',
    jsonMode: 'nested',
    delimiter: '.',
    maxDepth: 'unlimited'
  }
}, {
  headers: {
    'Content-Type': 'application/json'
  }
})
.then(response => {
  const result = response.data;
  if (!result.isError) {
    console.log('CSV Data:', result.data);
    // Save to file (Node.js)
    const fs = require('fs');
    fs.writeFileSync('output.csv', result.data);
  } else {
    console.log('Error:', result.msg);
  }
})
.catch(error => {
  console.error('Request failed:', error);
});

cURL Implementation

Standard Mode

curl -X POST https://mcp2.wtsolutions.cn/json-to-excel-api \
  -H "Content-Type: application/json" \
  -d '{"data": "[{\"name\": \"John\", \"age\": 30},{\"name\": \"Jane\", \"age\": 25}]"}'

Pro Mode

curl -X POST https://mcp2.wtsolutions.cn/json-to-excel-api \
  -H "Content-Type: application/json" \
  -d '{
    "data": "[{\"name\":\"John\",\"contact\":{\"email\":\"john@example.com\",\"phone\":\"1234567890\"}}]",
    "options": {
      "proCode": "your-email@example.com",
      "jsonMode": "nested",
      "delimiter": ".",
      "maxDepth": "unlimited"
    }
  }'

Data Type Handling

The API automatically handles different JSON data types:

JSON Type	CSV Representation
Numbers	Numeric values in CSV
Booleans	'true'/'false' strings
Strings	Escaped and quoted if necessary
Arrays	JSON.stringify array string
Objects	JSON.stringify object string (unless using nested mode)

Error Handling

The API provides descriptive error messages for common issues:

Error Message	Cause
Invalid JSON format	Input data is not valid JSON
Empty JSON data	Input data is an empty JSON string
Network Error when fetching file	Error downloading file from URL
File not found	File at provided URL cannot be found
Server Internal Error	Unexpected server error
Invalid Pro Code	Pro Code is not valid or expired

Best Practices for Error Handling

1. Always Check isError Flag

   if result["isError"]:
       # Handle error
       print(f"Error: {result['msg']}")
   else:
       # Process successful response
       csv_data = result["data"]

2. Implement Retry Logic

   import time
   max_retries = 3
   for attempt in range(max_retries):
       try:
           response = requests.post(url, json=payload)
           result = response.json()
           if not result["isError"]:
               break
       except Exception as e:
           if attempt < max_retries - 1:
               time.sleep(2 ** attempt)  # Exponential backoff
           else:
               raise

3. Log Errors for Debugging

   import logging
   logging.basicConfig(level=logging.INFO)
   
   if result["isError"]:
       logging.error(f"API Error: {result['msg']}")
       logging.error(f"Request payload: {payload}")

CORS Considerations

When making requests from a web browser, you may encounter CORS (Cross-Origin Resource Sharing) issues. To handle CORS:

1. Use a Server-Side Proxy

   • Make API calls from your server, not directly from the browser
   • Your server forwards requests to JSON to Excel API
   • Client communicates with your server

2. Configure CORS Headers

   • Ensure your server properly handles CORS
   • Set appropriate headers for cross-origin requests

Use Cases

Use Case 1: Automated Report Generation

import requests
import schedule
import time

def generate_daily_report():
    # Fetch data from your API
    api_response = requests.get('https://your-api.com/data')
    json_data = api_response.json()
    
    # Convert to CSV using JSON to Excel API
    conversion_response = requests.post(
        'https://mcp2.wtsolutions.cn/json-to-excel-api',
        json={"data": json.dumps(json_data)}
    )
    
    result = conversion_response.json()
    
    if not result["isError"]:
        # Save CSV file
        with open(f"daily_report_{time.strftime('%Y%m%d')}.csv", "w") as f:
            f.write(result["data"])
        print("Report generated successfully")

# Schedule daily report generation
schedule.every().day.at("09:00").do(generate_daily_report)

while True:
    schedule.run_pending()
    time.sleep(60)

Use Case 2: Web Service Integration

// Express.js endpoint that converts JSON to Excel
app.post('/convert-to-excel', async (req, res) => {
  try {
    const jsonData = req.body.data;
    
    // Call JSON to Excel API
    const response = await axios.post(
      'https://mcp2.wtsolutions.cn/json-to-excel-api',
      {
        data: JSON.stringify(jsonData),
        options: {
          proCode: process.env.PRO_CODE,
          jsonMode: 'nested',
          delimiter: '.'
        }
      }
    );
    
    const result = response.data;
    
    if (!result.isError) {
      // Send CSV back to client
      res.setHeader('Content-Type', 'text/csv');
      res.send(result.data);
    } else {
      res.status(400).json({ error: result.msg });
    }
  } catch (error) {
    res.status(500).json({ error: 'Conversion failed' });
  }
});

Use Case 3: Data Pipeline Integration

import requests
import pandas as pd

def process_api_data(api_url):
    # Fetch data from external API
    response = requests.get(api_url)
    json_data = response.json()
    
    # Convert to CSV using JSON to Excel API
    conversion_response = requests.post(
        'https://mcp2.wtsolutions.cn/json-to-excel-api',
        json={
            "data": json.dumps(json_data),
            "options": {
                "proCode": "your-email@example.com",
                "jsonMode": "nested",
                "delimiter": "_"
            }
        }
    )
    
    result = conversion_response.json()
    
    if not result["isError"]:
        # Load CSV into pandas for further processing
        from io import StringIO
        df = pd.read_csv(StringIO(result["data"]))
        
        # Perform additional analysis
        print(df.describe())
        
        return df
    else:
        print(f"Error: {result['msg']}")
        return None

# Usage
df = process_api_data('https://api.example.com/data')

Performance Considerations

Rate Limiting

Be mindful of API rate limits:

• Implement appropriate delays between requests
• Use caching for repeated conversions
• Batch requests when possible

Large Data Handling

For large JSON datasets:

• Consider splitting data into smaller chunks
• Process asynchronously to avoid blocking
• Implement progress tracking for long-running conversions

Caching Strategy

Cache conversion results to avoid redundant API calls:

import hashlib
import json

def get_cache_key(json_data):
    return hashlib.md5(json.dumps(json_data).encode()).hexdigest()

cache = {}

def convert_with_cache(json_data):
    cache_key = get_cache_key(json_data)
    
    if cache_key in cache:
        return cache[cache_key]
    
    # Make API call
    response = requests.post(
        'https://mcp2.wtsolutions.cn/json-to-excel-api',
        json={"data": json.dumps(json_data)}
    )
    result = response.json()
    
    # Cache the result
    cache[cache_key] = result
    return result

Next Steps

Now that you understand how to use the JSON to Excel API programmatically, you're ready to explore the MCP Server integration. In our next post, we'll cover the MCP Server, which provides another way for developers to integrate JSON to Excel functionality into their workflows, particularly for those working with AI and automation tools.

Ready to integrate the API into your application? Start building your JSON to Excel integration today!