Developers Implementation Guides

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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.

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Available Implementation Methods

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OpenAI Integration

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

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Python SDK

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

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LangGraph Integration

Best for: Complex AI workflows and multi-step processes Build sophisticated AI agents with state management and complex decision trees using LangGraph. Key Features:

• Stateful conversations
• Multi-step workflows
• Conditional logic
• Tool chaining

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Quick Comparison

Feature	OpenAI	Python SDK	LangGraph
Setup Complexity	Low	Medium	Medium
Best For	AI Agents	Data Pipelines	Complex Workflows
Language	Python	Python	Python
Tool Discovery	Automatic	Manual	Automatic
Response Format	Structured	JSON	Structured
Async Support	✓	✓	✓
State Management	Limited	Custom	Built-in
Error Handling	Built-in	Manual	Built-in

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Getting Started

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Prerequisites

Before implementing AgentSource MCP, ensure you have:

1. AgentSource API Key - Get your API key
2. Development Environment - Python 3.8+ recommended
3. Basic Understanding - Familiarity with REST APIs and async programming

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Core Concepts

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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

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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

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Common Use Cases

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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

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Market Research

Analyze industries, competitors, and market trends:

• Company segmentation by size, revenue, location
• Technology adoption trends
• Competitive landscape analysis
• Industry-specific insights

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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

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Due Diligence

Comprehensive company analysis for investment or partnership decisions:

• Financial metrics and performance
• Leadership and organizational changes
• Technology infrastructure
• Growth indicators and challenges

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Best Practices

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Security

• Store API keys in environment variables
• Never commit credentials to version control
• Use HTTPS for all connections
• Implement proper error handling

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Performance

• Use batch operations when possible
• Implement pagination for large datasets
• Cache results when appropriate
• Monitor rate limits and quotas

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Development

• Start with small proof-of-concepts
• Test with sample data first
• Implement comprehensive logging
• Handle edge cases gracefully

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Need Help?

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Resources

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Support

• Technical Support: support@explorium.ai
• Community Forum: Coming Soon!

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What’s Next?

1. Choose your implementation method based on your use case
2. Follow the specific guide for detailed instructions
3. Start with simple queries and gradually increase complexity
4. Join our community to share experiences and get help

Ready to get started? Choose an implementation guide above orcontact our team for personalized recommendations.