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

🤖 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

Get Started with OpenAI →

🐍 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

Get Started with Python SDK →

🔗 LangGraph Integration

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

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

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

AgentSource MCP API Reference

💬 Support

🚀 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.