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Documentation Index

Fetch the complete documentation index at: https://developers.explorium.ai/llms.txt

Use this file to discover all available pages before exploring further.

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Overview

This guide demonstrates how to use AgentSource MCP (Model Context Protocol) with OpenAI’s API. MCP enables your AI agents to seamlessly access Explorium’s data through a standardized protocol, allowing for dynamic tool discovery and execution.

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Prerequisites

  • Python 3.8 or higher
  • OpenAI API key
  • AgentSource API key
  • Basic understanding of OpenAI’s API

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Installation

First, install the required package:
Bash
pip install openai

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Quick Start

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1. Initialize the OpenAI Client

Python
from openai import OpenAI

# Initialize the client with your OpenAI API key
client = OpenAI(api_key="your-openai-api-key")

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2. Configure AgentSource MCP

Create a response with MCP tools configured:
Python
response = client.responses.create(
    model="gpt-4.1",
    tools=[{
        "type": "mcp",
        "require_approval": "never",
        "server_label": "explorium",
        "server_url": "https://mcp.explorium.ai/mcp",
        "headers": {
            "api_key": "YOUR_AGENTSOURCE_API_KEY"
        }
    }],
    input="Your query here"
)

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3. Basic Example

Here’s a complete example that searches for companies:
Python
from openai import OpenAI

# Initialize client
client = OpenAI(api_key="your-openai-api-key")

# Make a request to find banks
response = client.responses.create(
    model="gpt-4.1",
    tools=[{
        "type": "mcp",
        "require_approval": "never",
        "server_label": "explorium",
        "server_url": "https://mcp.explorium.ai/mcp",
        "headers": {
            "api_key": "YOUR_AGENTSOURCE_API_KEY"
        }
    }],
    input="Find 10 banks in the US with less than 5000 employees that use Azure"
)

# The response will contain the results
print(response)

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Common Use Cases

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Finding Companies with Specific Criteria

Python
# Search for tech companies using specific technologies
response = client.responses.create(
    model="gpt-4.1",
    tools=[{
        "type": "mcp",
        "require_approval": "never",
        "server_label": "explorium",
        "server_url": "https://mcp.explorium.ai/mcp",
        "headers": {
            "api_key": "YOUR_AGENTSOURCE_API_KEY"
        }
    }],
    input="Find software companies in California with 100-500 employees using AWS"
)

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Finding Contacts at Specific Companies

Python
# Get engineering directors at Microsoft
response = client.responses.create(
    model="gpt-4.1",
    tools=[{
        "type": "mcp",
        "require_approval": "never",
        "server_label": "explorium",
        "server_url": "https://mcp.explorium.ai/mcp",
        "headers": {
            "api_key": "YOUR_AGENTSOURCE_API_KEY"
        }
    }],
    input="Get 50 directors of engineering at Microsoft along with their email addresses"
)

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Analyzing Specific Departments

Python
# Analyze VPs in a specific department
response = client.responses.create(
    model="gpt-4.1",
    tools=[{
        "type": "mcp",
        "require_approval": "never",
        "server_label": "explorium",
        "server_url": "https://mcp.explorium.ai/mcp",
        "headers": {
            "api_key": "YOUR_AGENTSOURCE_API_KEY"
        }
    }],
    input="Inspect all VPs in the Engineering department at Salesforce and highlight those working on Agentforce"
)

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Understanding the Response

The MCP integration automatically handles:
  • Tool discovery and listing
  • Parameter formatting
  • API calls to AgentSource
  • Response parsing
Your agent will receive structured data that it can process and present in a user-friendly format.

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Advanced Configuration

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Multi-turn Conversations

For follow-up queries in the same context:
Python
# Initial query
response1 = client.responses.create(
    model="gpt-4.1",
    tools=[{
        "type": "mcp",
        "require_approval": "never",
        "server_label": "explorium",
        "server_url": "https://mcp.explorium.ai/mcp",
        "headers": {
            "api_key": "YOUR_AGENTSOURCE_API_KEY"
        }
    }],
    input="Find all VPs at Tesla"
)

# Follow-up query
response2 = client.responses.create(
    model="gpt-4.1",
    tools=[{
        "type": "mcp",
        "require_approval": "never",
        "server_label": "explorium",
        "server_url": "https://mcp.explorium.ai/mcp",
        "headers": {
            "api_key": "YOUR_AGENTSOURCE_API_KEY"
        }
    }],
    input="Now get their email addresses",
    previous_response_id=response1.id
)

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Tool Approval Settings

You can control whether tools require approval before execution:
  • "never" - Tools execute automatically
  • "always" - Tools require user approval
  • "on_error" - Approval required only on errors

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Debugging and Monitoring

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Response Tracing

To understand the execution flow, you can inspect the response object which contains:
  • Tool calls made
  • Input parameters sent
  • Responses received
  • Agent’s interpretation

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Example Response Structure

Python
# Access the response outputs
for output in response.output:
    if output.type == "mcp_call":
        print(f"Tool called: {output.name}")
        print(f"Arguments: {output.arguments}")
        print(f"Result: {output.output}")
    elif output.type == "message":
        print(f"Agent response: {output.content}")

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Best Practices

  1. API Key Security: Never hardcode API keys in your code. Use environment variables:
    Python
    import os
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
  2. Clear Queries: Be specific in your queries for better results:
    • “Find 20 CTOs at fintech companies in New York with 50-200 employees”
    • “Find some tech people”
  3. Handle Rate Limits: Implement appropriate error handling and retries
  4. Optimize Requests: Batch related queries when possible to reduce API calls

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Available Tools

Through MCP, your agent automatically has access to all AgentSource tools including:
  • Company Search: Find businesses based on various criteria
  • Contact Discovery: Locate professionals with specific roles
  • Email Enrichment: Get contact information
  • Company Matching: Match and enrich company data
  • Prospect Matching: Match and enrich prospect data
  • And more: The full suite of AgentSource capabilities
The beauty of MCP is that tools are discovered dynamically - you don’t need to explicitly define them.

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Error Handling

Python
try:
    response = client.responses.create(
        model="gpt-4.1",
        tools=[{
            "type": "mcp",
            "require_approval": "never",
            "server_label": "explorium",
            "server_url": "https://mcp.explorium.ai/mcp",
            "headers": {
                "api_key": "YOUR_AGENTSOURCE_API_KEY"
            }
        }],
        input="Your query"
    )
except Exception as e:
    print(f"Error: {e}")

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Next Steps

  • Explore the complete AgentSource MCP documentation
  • Check out implementations using other frameworks (LangGraph, Python SDK)
  • Learn about advanced filtering and search capabilities
  • Understand rate limits and quotas

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Support

If you encounter any issues or have questions: Note: Remember to replaceYOUR_AGENTSOURCE_API_KEY and your-openai-api-key with your actual API keys.