OpenAI Implementation Guide

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.

Prerequisites

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

Installation

First, install the required package:

Bash

pip install openai

Quick Start

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")

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"
)

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)

Common Use Cases

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"
)

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"
)

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"
)

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.

Advanced Configuration

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
)

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

Debugging and Monitoring

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

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}")

Best Practices

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"))
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”
Handle Rate Limits: Implement appropriate error handling and retries
Optimize Requests: Batch related queries when possible to reduce API calls

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.

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}")

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

Support

If you encounter any issues or have questions:

Contact our support team at support@explorium.ai
Join our developer community

Note: Remember to replaceYOUR_AGENTSOURCE_API_KEY and your-openai-api-key with your actual API keys.

​Overview

​Prerequisites

​Installation

​Quick Start

​1. Initialize the OpenAI Client

​2. Configure AgentSource MCP

​3. Basic Example

​Common Use Cases

​Finding Companies with Specific Criteria

​Finding Contacts at Specific Companies

​Analyzing Specific Departments

​Understanding the Response

​Advanced Configuration

​Multi-turn Conversations

​Tool Approval Settings

​Debugging and Monitoring

​Response Tracing

​Example Response Structure

​Best Practices

​Available Tools

​Error Handling

​Next Steps

​Support

Overview

Prerequisites

Installation

Quick Start

1. Initialize the OpenAI Client

2. Configure AgentSource MCP

3. Basic Example

Common Use Cases

Finding Companies with Specific Criteria

Finding Contacts at Specific Companies

Analyzing Specific Departments

Understanding the Response

Advanced Configuration

Multi-turn Conversations

Tool Approval Settings

Debugging and Monitoring

Response Tracing

Example Response Structure

Best Practices

Available Tools

Error Handling

Next Steps

Support