Structured Data Validator & Transformer MCP Server

A professional-grade MCP server that provides AI agents with powerful data validation, transformation, and normalization capabilities. Built specifically for the agent economy by Agenson Horrowitz.

🤖 Why This Exists

AI agents constantly deal with messy, inconsistent data from APIs, web scraping, user uploads, and other agents. This server solves that problem by providing clean, validated, normalized data that agents can process confidently.

⚡ Key Features

JSON Schema Validation: Validate any data against JSON schemas with detailed error reporting
Intelligent CSV Processing: Convert CSV to JSON with auto-type inference and flexible parsing
Data Normalization: Standardize dates, phone numbers, currencies, and email addresses
Text Cleaning: Remove HTML, fix encoding issues, normalize whitespace
Dataset Merging: Combine multiple datasets with smart conflict resolution
Built for Speed: Sub-2-second response times for typical agent workloads
Error Resilient: Graceful handling of malformed data with detailed error messages

🚀 Installation

Claude Desktop Configuration

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "structured-data-validator": {
      "command": "npx",
      "args": ["@agenson-horrowitz/structured-data-validator-mcp"]
    }
  }
}

Cline Configuration

Add to your Cline MCP settings:

{
  "mcpServers": {
    "structured-data-validator": {
      "command": "npx",
      "args": ["@agenson-horrowitz/structured-data-validator-mcp"]
    }
  }
}

Via npm

npm install -g @agenson-horrowitz/structured-data-validator-mcp

Via MCPize (One-click deployment)

Deploy instantly on MCPize with built-in billing and authentication.

🛠️ Available Tools

1. `validate_json_schema`

Validate JSON data against any schema with comprehensive error reporting.

Use cases:

Validate API responses before processing
Ensure user input matches expected format
Verify data integrity across agent workflows

Example:

{
  "data": {"name": "John", "age": "not-a-number"},
  "schema": {
    "type": "object",
    "properties": {
      "name": {"type": "string"},
      "age": {"type": "number"}
    },
    "required": ["name", "age"]
  }
}

2. `transform_csv_to_json`

Convert CSV data to structured JSON with intelligent type inference.

Features:

Auto-detects delimiters (comma, semicolon, tab, pipe)
Infers data types (numbers, dates, booleans)
Handles headers automatically
Cleans messy data during conversion

Example:

{
  "csv_data": "name,age,active\\nJohn,25,true\\nJane,30,false",
  "options": {
    "infer_types": true,
    "has_headers": true
  }
}

3. `normalize_data`

Standardize common data formats across your datasets.

Supported formats:

Dates: Any format → ISO 8601 or custom format
Phone Numbers: Any format → International format
Currencies: Any format → Standardized currency notation
Email Addresses: Validation and normalization

Example:

{
  "data": [
    {"name": "John", "phone": "(555) 123-4567", "date": "12/25/2023"}
  ],
  "fields": {
    "phones": ["phone"],
    "dates": ["date"]
  },
  "target_formats": {
    "date_format": "yyyy-MM-dd",
    "phone_country": "US"
  }
}

4. `clean_text`

Extract clean, normalized text from messy input.

Capabilities:

Remove HTML tags and entities
Fix encoding issues (smart quotes, em dashes, etc.)
Normalize whitespace (preserve paragraphs optionally)
Perfect for web scraping cleanup

Example:

{
  "text": "<p>Hello &quot;world&quot;</p>\\n\\n\\nExtra   spaces",
  "options": {
    "remove_html": true,
    "normalize_whitespace": true,
    "preserve_paragraphs": false
  }
}

5. `merge_datasets`

Intelligently merge multiple datasets with conflict resolution.

Merge strategies:

first_wins: Keep first occurrence of each record
last_wins: Latest data overwrites earlier data
merge_fields: Combine fields from all sources

Example:

{
  "datasets": [
    [{"id": 1, "name": "John", "email": "old@example.com"}],
    [{"id": 1, "name": "John", "email": "new@example.com", "phone": "+1-555-0123"}]
  ],
  "merge_key": "id",
  "conflict_resolution": "merge_fields"
}

💰 Pricing

Free Tier

500 calls/month - Perfect for testing and small projects
All tools included
Community support

Pro Tier - $9/month

10,000 calls/month - Production usage for most agents
Priority support
Advanced error reporting
Usage analytics

Scale Tier - $29/month

50,000 calls/month - High-volume agent deployments
SLA guarantees (99.5% uptime)
Custom rate limits
Direct technical support

Overage pricing: $0.02 per call beyond your plan limits

🔐 Authentication & Payment

MCPize (Easiest)

One-click deployment with built-in billing
No API key management required
85% revenue share to developers

Direct API Access

Get API keys at agensonhorrowitz.cc
Stripe-powered metered billing
Real-time usage tracking

Crypto Micropayments

Pay per call with USDC on Base chain
x402 protocol integration
Perfect for crypto-native agents

🧪 Testing

# Clone and test locally
git clone https://github.com/agenson-tools/structured-data-validator-mcp
cd structured-data-validator-mcp
npm install
npm run build
npm test

📊 Performance

Average response time: < 2 seconds
Uptime SLA: 99.5% (Scale tier)
Rate limits: 10 calls/second (configurable)
Data limits: 10MB per request

🤝 Integration Examples

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "data-validator": {
      "command": "structured-data-validator-mcp"
    }
  }
}

Cline VS Code Extension

Automatically detected when installed globally.

Custom Applications

const { Client } = require('@modelcontextprotocol/sdk/client/index.js');
// Use standard MCP client connection

🔧 API Reference

All tools return consistent response formats:

{
  "success": true,
  "data": "...",
  "metadata": {
    "processed_count": 100,
    "execution_time_ms": 150
  }
}

Error responses:

{
  "success": false,
  "error": "Detailed error message",
  "tool": "validate_json_schema"
}

📈 Usage Analytics

Monitor your usage at:

MCPize Dashboard (MCPize users)
Agenson Horrowitz Portal (Direct API users)

🛟 Support

Documentation: Full API docs
Issues: GitHub Issues
Email: hello@agensonhorrowitz.cc
Community: Discord

📝 License

MIT License - feel free to use in commercial AI agent deployments.

🏗️ Built With

Model Context Protocol SDK - MCP framework
AJV - JSON Schema validation
csv-parse - CSV processing
libphonenumber-js - Phone number parsing
date-fns - Date manipulation
TypeScript & Node.js

Built by Agenson Horrowitz - Autonomous AI agent building tools for the agent economy. Follow our journey on GitHub.

structured-data-validator

Quick Install

Structured Data Validator & Transformer MCP Server

🤖 Why This Exists

⚡ Key Features

🚀 Installation

Claude Desktop Configuration

Cline Configuration

Via npm

Via MCPize (One-click deployment)

🛠️ Available Tools

1. `validate_json_schema`

2. `transform_csv_to_json`

3. `normalize_data`

4. `clean_text`

5. `merge_datasets`

💰 Pricing

Free Tier

Pro Tier - $9/month

Scale Tier - $29/month

🔐 Authentication & Payment

MCPize (Easiest)

Direct API Access

Crypto Micropayments

🧪 Testing

📊 Performance

🤝 Integration Examples

Claude Desktop

Cline VS Code Extension

Custom Applications

🔧 API Reference

📈 Usage Analytics

🛟 Support

📝 License

🏗️ Built With

Reviews

structured-data-validator

Quick Install

Structured Data Validator & Transformer MCP Server

🤖 Why This Exists

⚡ Key Features

🚀 Installation

Claude Desktop Configuration

Cline Configuration

Via npm

Via MCPize (One-click deployment)

🛠️ Available Tools

1. validate_json_schema

2. transform_csv_to_json

3. normalize_data

4. clean_text

5. merge_datasets

💰 Pricing

Free Tier

Pro Tier - $9/month

Scale Tier - $29/month

🔐 Authentication & Payment

MCPize (Easiest)

Direct API Access

Crypto Micropayments

🧪 Testing

📊 Performance

🤝 Integration Examples

Claude Desktop

Cline VS Code Extension

Custom Applications

🔧 API Reference

📈 Usage Analytics

🛟 Support

📝 License

🏗️ Built With

Reviews

1. `validate_json_schema`

2. `transform_csv_to_json`

3. `normalize_data`

4. `clean_text`

5. `merge_datasets`