Choose the right implementation method for integrating AgentSource MCP into your applications and workflows.
Overview
AgentSource MCP (Model Context Protocol) provides a standardized way to access Explorium's comprehensive business and contact data. Whether you're building AI agents, data pipelines, or custom applications, we offer multiple implementation paths to suit your needs.
Available Implementation Methods
Best for: AI-powered applications and conversational agents
Build intelligent agents that can automatically discover and use AgentSource tools through OpenAI's API.
Key Features:
- Automatic tool discovery
- Natural language queries
- Built-in response formatting
- Minimal setup required
Use Cases:
- Customer service chatbots
- Sales intelligence assistants
- Automated research agents
- Interactive data exploration
Best for: Direct programmatic access and custom applications
Native Python implementation for developers who need fine-grained control over data access and processing.
Key Features:
- Asynchronous operations
- Full control over tool execution
- Ideal for data pipelines
- Comprehensive error handling
Use Cases:
- Data enrichment pipelines
- Batch processing systems
- Custom analytics tools
- Integration with existing Python applications
Best for: Complex AI workflows and multi-step processes
Coming Soon
Build sophisticated AI agents with state management and complex decision trees using LangGraph.
Key Features:
- Stateful conversations
- Multi-step workflows
- Conditional logic
- Tool chaining
Best for: Language-agnostic implementations
Coming Soon
Direct HTTP/SSE integration for any programming language or platform.
Key Features:
- Platform independent
- Full API access
- Custom implementations
- Maximum flexibility
Quick Comparison
| Feature | OpenAI | Python SDK | LangGraph | Direct API |
|---|---|---|---|---|
| Setup Complexity | Low | Medium | Medium | High |
| Best For | AI Agents | Data Pipelines | Complex Workflows | Custom Solutions |
| Language | Python | Python | Python | Any |
| Tool Discovery | Automatic | Manual | Automatic | Manual |
| Response Format | Structured | JSON | Structured | JSON |
| Async Support | ✓ | ✓ | ✓ | ✓ |
| State Management | Limited | Custom | Built-in | Custom |
| Error Handling | Built-in | Manual | Built-in | Manual |
Getting Started
Prerequisites
Before implementing AgentSource MCP, ensure you have:
- AgentSource API Key - Get your API key
- Development Environment - Python 3.8+ recommended
- Basic Understanding - Familiarity with REST APIs and async programming
Core Concepts
What is MCP?
Model Context Protocol (MCP) is a standardized protocol that enables AI models and applications to discover and use tools dynamically. With AgentSource MCP, you get:
- Dynamic Tool Discovery - Tools are discovered at runtime, not hardcoded
- Standardized Interface - Consistent API across all tools
- Type Safety - Structured inputs and outputs
- Streaming Support - Real-time data updates via SSE
Available Tool Categories
Business Intelligence
- Company search and matching
- Firmographic enrichment
- Technology stack analysis
- Financial metrics (public companies)
- Competitive landscape insights
Contact Discovery
- Employee search by role/department
- Contact information enrichment
- Professional profile data
- Work history and experience
- Social media activity
Market Intelligence
- Industry trends and statistics
- Company events and news
- Website changes monitoring
- Workforce trend analysis
- Funding and acquisition data
Common Use Cases
🎯 Sales Intelligence
Find prospects at target companies, enrich contact information, and track company events:
- Identify decision makers at target accounts
- Get verified email addresses and phone numbers
- Monitor job changes and company updates
- Analyze technology stack for better positioning
📊 Market Research
Analyze industries, competitors, and market trends:
- Company segmentation by size, revenue, location
- Technology adoption trends
- Competitive landscape analysis
- Industry-specific insights
🤝 Lead Enrichment
Enhance your CRM data with comprehensive business and contact information:
- Bulk company matching and enrichment
- Contact verification and updates
- Organizational structure mapping
- Technographic segmentation
🔍 Due Diligence
Comprehensive company analysis for investment or partnership decisions:
- Financial metrics and performance
- Leadership and organizational changes
- Technology infrastructure
- Growth indicators and challenges
Best Practices
🔐 Security
- Store API keys in environment variables
- Never commit credentials to version control
- Use HTTPS for all connections
- Implement proper error handling
⚡ Performance
- Use batch operations when possible
- Implement pagination for large datasets
- Cache results when appropriate
- Monitor rate limits and quotas
🛠️ Development
- Start with small proof-of-concepts
- Test with sample data first
- Implement comprehensive logging
- Handle edge cases gracefully
Need Help?
📚 Resources
💬 Support
- Technical Support: [email protected]
- Community Forum: Coming Soon!
🚀 What's Next?
- Choose your implementation method based on your use case
- Follow the specific guide for detailed instructions
- Start with simple queries and gradually increase complexity
- Join our community to share experiences and get help
Ready to get started? Choose an implementation guide above orcontact our team for personalized recommendations.