Approaches to convert it to an LLM+MCP (Model Context Protocol) system

Here are the main options and recommendations:

Conversion Options

1. Direct Form-to-API Mapping

Convert each form field to structured API parameters:

// MCP tool definition
{
  name: "create_support_case",
  description: "Create a support case in system",
  inputSchema: {
    type: "object",
    properties: {
      // Contact Information
      contactName: { type: "string", description: "Contact person name" },
      email: { type: "string", description: "Contact email address" },
      phone: { type: "string", description: "Contact phone number" },
      preferredContact: {
        type: "string",
        enum: ["None", "Email", "Phone"],
        description: "Preferred contact method"
      },
      additionalEmail: { type: "string", description: "Additional contact email" },

      // Case Details
      subject: { type: "string", description: "Case subject line" },
      supportProduct: { type: "string", description: "Product needing support" },
      description: { type: "string", description: "Detailed issue description" },
      operatingSystem: { type: "string", description: "Operating system" },
      browser: { type: "string", description: "Internet browser" },

      // Options
      suppressEmails: { type: "boolean", default: false },
      priority: { type: "string", enum: ["Low", "Medium", "High", "Critical"] },
      attachments: { type: "array", items: { type: "string" } }
    },
    required: ["contactName", "email", "subject", "description"]
  }
}

2. Natural Language Processing Approach

Let the LLM extract information from conversational input:

// MCP tool for intelligent case creation
{
  name: "create_case_from_conversation",
  description: "Create support case from natural language description",
  inputSchema: {
    type: "object",
    properties: {
      conversationText: {
        type: "string",
        description: "Natural language description of the issue and contact details"
      },
      userPreferences: {
        type: "object",
        properties: {
          suppressEmails: { type: "boolean", default: false },
          priority: { type: "string" },
          preferredContact: { type: "string" }
        }
      }
    }
  }
}

3. Hybrid Interactive Approach

Combine structured data collection with conversational flow:

// Multi-step MCP implementation
const caseCreationTools = [
  {
    name: 'collect_contact_info',
    description: 'Collect and validate contact information',
  },
  {
    name: 'analyze_issue_description',
    description: 'Process issue description and categorize',
  },
  {
    name: 'suggest_product_category',
    description: 'Suggest appropriate product/category based on description',
  },
  {
    name: 'create_final_case',
    description: 'Create the support case with all collected information',
  },
];

Best Approach Recommendation

Recommended: Multi-Tool MCP Strategy

This approach breaks down the form into logical components and uses multiple MCP tools:

// 1. Contact Information Collection
async function collectContactInfo(userInput) {
  // Extract name, email, phone from natural language
  // Validate email format
  // Determine preferred contact method
}

// 2. Issue Analysis and Categorization
async function analyzeIssue(description) {
  // Use LLM to categorize the issue
  // Suggest appropriate product/service
  // Determine priority level
  // Extract technical details (OS, browser, etc.)
}

// 3. Case Creation with Options
async function createSupportCase(caseData, options = {}) {
  const {
    suppressEmails = false,
    autoAssign = true,
    notifyManager = false,
    attachments = [],
  } = options;

  // Map to API structure
  const aCase = {
    contact: {
      name: caseData.contactName,
      email: caseData.email,
      phone: caseData.phone,
      preferredContact: caseData.preferredContact,
    },
    case: {
      subject: caseData.subject,
      description: caseData.description,
      product: caseData.supportProduct,
      priority: caseData.priority,
      operatingSystem: caseData.operatingSystem,
      browser: caseData.browser,
    },
    options: {
      suppressEmails,
      autoAssign,
      notifyManager,
    },
  };

  // Call API
  return await anAPI.createCase(aCase);
}

Usage Example:

// User input: "Hi, I'm John from Acme Corp. I'm having trouble with OneSite -
// the dashboard won't load in Chrome on Windows 10. My email is john@acme.com"

// LLM processes this and calls MCP tools:
const contactInfo = await collectContactInfo(userInput);
const issueAnalysis = await analyzeIssue(userInput);
const caseResult = await createSupportCase(
  {
    ...contactInfo,
    ...issueAnalysis,
  },
  {
    suppressEmails: false,
    autoAssign: true,
  },
);

Implementation Benefits

Advantages of This Approach:

1. Natural Interaction - Users can describe issues conversationally
2. Intelligent Extraction - LLM extracts structured data from unstructured input
3. Flexible Options - Support for advanced case creation options
4. Validation - Built-in validation and error handling
5. Extensible - Easy to add new fields or options

Additional Features to Consider:

• Attachment Handling - File upload and processing
• Case Templates - Pre-defined templates for common issues
• Follow-up Automation - Automatic status updates and notifications
• Integration Points - Connect with existing ticketing systems
• Analytics - Track case creation patterns and success rates

This approach transforms the static form into an intelligent, conversational interface while maintaining all the functionality and options of the original system.