MCP Server
MCP Server - Introduction
What is the Tomba MCP Server?
The Tomba MCP Server is a Model Context Protocol (MCP) server that provides seamless integration with Tomba.io's powerful email discovery and verification APIs. It allows Large Language Models (LLMs) and AI applications to access comprehensive email intelligence capabilities through a standardized protocol.
Purpose & Benefits
For AI Applications
- Structured Access: Provides LLMs with reliable, structured access to email data
- Real-time Intelligence: Get up-to-date email verification and discovery results
- Scalable Integration: Handle multiple concurrent requests with proper error handling
- Protocol Standardization: Uses MCP for consistent tool interfaces across different AI systems
For Developers
- Easy Integration: Simple JSON-RPC interface for any MCP-compatible client
- Type Safety: Full TypeScript implementation with comprehensive type definitions
- Comprehensive Testing: Extensive test suite and debugging tools
- Professional Grade: Production-ready with proper error handling and logging
Architecture Overview
Code
Components
- MCP Server Core: Handles protocol communication and request routing
- Tomba Client Wrapper: Abstracts Tomba.io SDK with error handling
- Tool Registry: Nine specialized tools for different email intelligence tasks
- Type System: Complete TypeScript definitions for all data structures
Core Capabilities
Email Discovery
- Domain Search: Find all emails associated with any domain
- Email Finder: Generate likely email addresses from names + domain
- Author Finder: Extract author emails from article URLs
- LinkedIn Finder: Discover emails from LinkedIn profile URLs
Email Intelligence
- Email Verifier: Check deliverability and validate email addresses
- Email Enrichment: Get comprehensive contact information
- Company Discovery: Find relevant companies and their contact data
Phone Intelligence
- Phone Finder: Search phone numbers by email, domain, or LinkedIn
- Phone Validator: Validate phone numbers and get carrier information
Model Context Protocol (MCP)
What is MCP?
The Model Context Protocol is an open standard that enables secure connections between AI applications and external data sources. It provides:
- Standardized Communication: Consistent JSON-RPC based protocol
- Security: Controlled access to external APIs and data
- Flexibility: Works with any MCP-compatible LLM or AI application
- Extensibility: Easy to add new tools and capabilities
Why MCP for Email Intelligence?
- Real-time Data: Access live email verification and discovery data
- Structured Responses: Consistent data formats for reliable AI processing
- Error Handling: Graceful handling of API limits and errors
- Scalability: Handle multiple concurrent requests efficiently
Use Cases
For Sales & Marketing
- Lead Generation: Find contact information for prospects
- Email Validation: Verify email lists before campaigns
- Contact Enrichment: Enhance existing contact databases
- Competitive Intelligence: Research competitor contact information
For Recruitment
- Candidate Research: Find contact information for potential hires
- Company Intelligence: Research target organizations
- Contact Verification: Validate candidate-provided information
For Research & Journalism
- Source Discovery: Find expert contacts in specific industries
- Fact Checking: Verify contact information and company details
- Interview Sourcing: Locate article authors and subject matter experts
For Data Quality
- Database Cleaning: Validate and enrich existing contact data
- Duplicate Detection: Identify and merge duplicate contact records
- Information Verification: Cross-reference contact details across sources
🔧 Technical Specifications
Supported Protocols
- MCP Version: 2024-11-05
- Transport: stdio (standard input/output)
- Message Format: JSON-RPC 2.0
- Authentication: API key + secret key
System Requirements
- Node.js: 16.0.0 or higher
- TypeScript: 5.2+ (for development)
- Memory: Minimum 512MB RAM
- Network: Internet connection for Tomba.io API access
API Rate Limits
- Respects Tomba.io rate limiting
- Handles 429 responses gracefully
- Implements exponential backoff for retries
- Provides clear error messages for limit exceeded
Quick Start Example
Here's how the MCP server integrates with an AI application:
Code
Data Quality & Reliability
Data Sources
- Tomba.io Database: 400+ million email addresses
- Real-time Verification: Live deliverability checking
- Multiple Sources: Web crawling, social networks, public databases
- Regular Updates: Continuously updated data sources
Accuracy Metrics
- Email Discovery: 95%+ accuracy for common domains
- Verification Results: Real-time SMTP validation
- Confidence Scores: Numerical confidence ratings for results
- Source Attribution: Full source tracking for discovered emails
Privacy & Compliance
- GDPR Compliant: Respects data protection regulations
- Opt-out Respect: Honors email opt-out requests
- No Storage: Server doesn't store or cache personal data
- Secure Transport: All communications over secure channels
Future Roadmap
Planned Features
- Webhook Support: Real-time notifications for data changes
- Bulk Operations: Batch processing for large datasets
- Custom Filtering: Advanced search and filtering capabilities
- Integration Templates: Pre-built integrations for popular platforms
Enhanced Intelligence
- AI-Powered Matching: Improved email pattern recognition
- Social Media Integration: Extended social profile discovery
- Company Insights: Enhanced company intelligence and metrics
- Predictive Scoring: ML-powered deliverability predictions
Next Steps
- Tools Reference: Detailed documentation for each tool
- LLM Integration: Connect with popular AI applications
Support & Resources
- GitHub Repository: tomba-mcp-server
- Tomba.io Documentation: developer.tomba.io
- MCP Specification: modelcontextprotocol.io
- Issue Tracker: Report bugs and request features on GitHub
The Tomba MCP Server bridges the gap between AI applications and professional email intelligence, enabling smarter, data-driven decisions in sales, marketing, recruitment, and research workflows.
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