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", "

... [View full README on GitHub](https://github.com/agenson-tools/structured-data-validator-mcp#readme)