🤖 Parserator MCP Server

Intelligent data parsing for AI agents via Model Context Protocol

Transform any unstructured data into structured JSON using Parserator's revolutionary Architect-Extractor pattern - now available as a standardized MCP tool that any AI agent can use.

🚀 What is this?

The Parserator MCP Server exposes Parserator's intelligent parsing capabilities through the Model Context Protocol (MCP), making it available to any AI agent that supports MCP.

Why MCP + Parserator?

• Universal Compatibility: Works with Claude Desktop, LangChain, CrewAI, AutoGPT, and any MCP-compatible agent
• Zero Vendor Lock-in: MCP is an open standard - switch agents freely while keeping your parsing tools
• Production Ready: Built on Parserator's proven Architect-Extractor pattern with 95%+ accuracy
• Token Efficient: 70% fewer tokens than naive LLM prompting approaches

🏗️ The Architect-Extractor Advantage

Unlike simple "prompt an LLM to output JSON" approaches, Parserator uses a sophisticated two-stage process:

1. 🏛️ The Architect: Plans the parsing strategy using a small data sample
2. ⚡ The Extractor: Executes the plan on full data with surgical precision

Result: Higher accuracy, lower token costs, more reliable outputs.

📦 Installation & Setup

Quick Start

# Install globally
npm install -g parserator-mcp-server

# Run with your API key
parserator-mcp-server pk_live_your_api_key_here

Get Your API Key

1. Visit parserator.com
2. Sign up for free account
3. Generate API key from dashboard
4. Start with 1,000 free parses per month

Claude Desktop Integration

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "parserator": {
      "command": "npx",
      "args": ["parserator-mcp-server", "pk_live_your_api_key"],
      "description": "Intelligent data parsing for AI agents"
    }
  }
}

Other Agent Frameworks

The MCP server works with any MCP-compatible system:

• LangChain: Use MCP tool integration
• CrewAI: Add as MCP skill
• AutoGPT: Plugin via MCP adapter
• Custom Agents: Standard MCP protocol

🛠️ Available Tools

parse_data - Main Parsing Tool

Transform unstructured data into structured JSON:

// Example: Email processing
{
  "inputData": "From: [email protected]\nSubject: Q4 Report Due\nHi team, please finish the quarterly report by Dec 15th.",
  "outputSchema": {
    "sender": "string",
    "subject": "string", 
    "deadline": "string",
    "priority": "string",
    "tasks": "string_array"
  },
  "instructions": "Extract actionable information"
}

// Result:
{
  "success": true,
  "parsedData": {
    "sender": "[email protected]",
    "subject": "Q4 Report Due",
    "deadline": "December 15th",
    "priority": "normal",
    "tasks": ["finish quarterly report"]
  },
  "metadata": {
    "confidence": 0.96,
    "tokensUsed": 245,
    "processingTime": 850
  }
}

suggest_schema - AI-Powered Schema Generation

Let AI suggest the optimal structure for your data:

{
  "sampleData": "John Smith, Senior Engineer, [email protected], (555) 123-4567"
}

// Result:
{
  "suggestedSchema": {
    "name": "string",
    "title": "string", 
    "email": "string",
    "phone": "string"
  },
  "confidence": 0.91
}

validate_schema - Data Validation

Ensure parsed data matches expected structure:

{
  "data": {"name": "John", "email": "[email protected]"},
  "schema": {"name": "string", "email": "string", "phone": "string"}
}

// Result:
{
  "valid": false,
  "errors": ["Missing required field: phone"],
  "suggestions": ["Add phone field or mark as optional"]
}

test_connection - Health Check

Verify API connectivity and account status:

// No parameters needed

// Result:
{
  "connection": "healthy",
  "latency": "245ms",
  "apiEndpoint": "https://app-5108296280.us-central1.run.app"
}

📊 Available Resources

parserator://usage - API Usage Stats

{
  "requestsThisMonth": 1250,
  "tokensUsedThisMonth": 125000,
  "quotaRemaining": 875,
  "billingPeriod": {
    "start": "2024-01-01",
    "end": "2024-01-31"
  }
}

parserator://templates - Pre-built Patterns

[
  {
    "id": "email-processor",
    "name": "Email Processor", 
    "description": "Extract structured data from emails",
    "schema": {
      "sender": "string",
      "subject": "string",
      "tasks": "string_array",
      "priority": "string"
    }
  }
]

parserator://examples - Usage Examples

Complete examples for common use cases with expected inputs/outputs.

🎯 Common Use Cases

📧 Email Processing Agents

// Parse emails to extract actionable items
const emailResult = await callTool('parserator', 'parse_data', {
  inputData: rawEmailContent,
  outputSchema: {
    sender: 'string',
    recipients: 'string_array', 
    tasks: 'string_array',
    dates: 'string_array',
    priority: 'string',
    summary: 'string'
  }
});

📄 Document Analysis Agents

// Extract key information from documents
const docResult = await callTool('parserator', 'parse_data', {
  inputData: documentText,
  outputSchema: {
    title: 'string',
    author: 'string',
    keyPoints: 'string_array',
    entities: 'array',
    sentiment: 'string'
  }
});

🧾 Invoice Processing Agents

// Parse invoices for business automation
const invoiceResult = await callTool('parserator', 'parse_data', {
  inputData: invoiceText,
  outputSchema: {
    vendor: 'string',
    amount: 'number',
    date: 'string',
    items: 'array',
    invoiceNumber: 'string'
  }
});

🕷️ Web Scraping Agents

// Normalize data from different website structures
const webResult = await callTool('parserator', 'parse_data', {
  inputData: scrapedContent,
  outputSchema: {
    title: 'string',
    price: 'number',
    description: 'string',
    features: 'string_array',
    availability: 'boolean'
  }
});

🔧 Advanced Configuration

Environment Variables

# Optional: Custom API endpoint
PARSERATOR_BASE_URL=https://your-custom-endpoint.com

# Optional: Request timeout (default: 30000ms)
PARSERATOR_TIMEOUT=60000

# Optional: Enable debug logging
PARSERATOR_DEBUG=true

Programmatic Usage

import { ParseratorMCPServer } from 'parserator-mcp-server';

const server = new ParseratorMCPServer('pk_live_your_api_key');
await server.start();

🚀 Performance & Reliability

Built for Production

• High Accuracy: 95%+ success rate on structured data extraction
• Fast Response: <3 second average processing time
• Token Efficient: 70% fewer tokens than naive approaches
• Reliable: 99.9% uptime with automatic retries

Error Handling

• Graceful degradation with fallback methods
• Detailed error messages with suggestions
• Automatic retry logic for transient failures
• Comprehensive logging and debugging support

🌟 Why Choose Parserator MCP?

vs. Manual Prompting

❌ Manual: "Extract this data as JSON" (unreliable, high tokens)
✅ Parserator: Architect-Extractor pattern (reliable, efficient)

vs. Rigid Libraries

❌ Libraries: Fixed schemas, brittle parsing
✅ Parserator: Flexible schemas, intelligent adaptation

vs. Vendor Lock-in

❌ Proprietary: Locked to specific platforms
✅ MCP: Universal standard, freedom to move

🤝 Contributing & Community

• GitHub: parserator/mcp-server
• Issues: Report bugs
• Discord: Join the community
• Docs: Full documentation

📄 License

MIT License - see LICENSE file for details.

🏢 Enterprise & Support

• Enterprise Plans: Custom SLAs, on-premise deployment
• Priority Support: Dedicated support channels
• Custom Integration: Help with complex parsing needs
• Training: Team workshops and best practices

Contact: [email protected]

Built with ❤️ by the Parserator team
Making AI agents smarter, one parse at a time

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