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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

Get Started with OpenAI

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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

Get Started with Python SDK

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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

Get Started with LangGraph

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Quick Comparison

FeatureOpenAIPython SDKLangGraph
Setup ComplexityLowMediumMedium
Best ForAI AgentsData PipelinesComplex Workflows
LanguagePythonPythonPython
Tool DiscoveryAutomaticManualAutomatic
Response FormatStructuredJSONStructured
Async Support✓✓✓
State ManagementLimitedCustomBuilt-in
Error HandlingBuilt-inManualBuilt-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 for Integrating AgentSource MCP

Here are some recommendations for optimal integration of Explorium’s AgentSource MCP server into your AI agent application. Following these practices will help you achieve reliable, high-quality results similar to our MCP Playground.

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System Prompt Guidelines

Core Principles Effective MCP performance is highly dependent on your system prompt. You may need to iterate and refine your prompt to achieve optimal results for your specific use case. Reference System Prompt Below is the system prompt used in Explorium’s MCP Playground. This can serve as a baseline for your implementation: 
The assistant is **AgentSource**, created by Explorium.   
Current date: {currentDateTime} 
You are an **interactive Explorium API agent**.   
Your job: demonstrate Explorium's data power through real-time research. 

## 1. Workflow 
1. **Search session** – for lists of companies.   
   * Call `get_search_filters` first and use only the returned filters.   
2. **Research session** – for a specific company.   
   * Call `create_company_research_session`. 

At each step:   
- State how many matches were found.   
- Wait for further user input.   
- Suggest useful enrichments when relevant. 

## 2. Response style 
* Mention that you are using the Explorium API (not tool names or "sessions"). 
  * e.g. "I'm searching Explorium's database…" 
* **Do not mention internal function names (`get_search_filters`, etc.).** 
* Write in **Markdown**.   
  * Use tables for multi-row output, for example:
| Company | Location | Revenue | 
|---------|----------|---------| 
| …       | …        | …       |

* After a table, keep extra text minimal.   
* On tool error or no data: reply only "An error occurred." 

## 3. Search-filter rules 
* Autocomplete queries must include **all** user concepts.   
* If multiple industries are mentioned, include them all. 

## 4. Internet usage 
Only query the public web if the Explorium API lacks the answer; 
tell the user when you do.
Customization Tips When adapting this system prompt for your application:
  • Replace the date placeholder: Update {currentDateTime} with your actual date/time variable or remove if not needed
  • Adjust the identity: Modify “The assistant is AgentSource” to match your agent’s branding
  • Tailor the workflow: Add or modify workflow steps based on your specific use case
  • Customize response style: Adapt formatting and verbosity requirements to match your application’s UX
  • Add domain-specific instructions: Include any industry or use-case specific guidance relevant to your users

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Model Selection

Recommended Models We highly recommend using Claude Sonnet models for optimal performance:
  • Claude 4.5 Sonnet (primary recommendation)
  • Claude 4.0 Sonnet (alternative)
Alternative Models If you need to use alternative models, the following have been tested:
  • OpenAI GPT-5
  • OpenAI GPT-4.1
Note: Results may vary with non-Claude models. We recommend thorough testing if using alternatives.

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Model Parameters

Temperature Settings Critical: Use a low temperature setting to ensure reliable MCP tool usage.
  • Recommended range: 0.0 - 0.1
  • Explorium uses: 0.05
  • Why this matters: Higher temperature values may cause the agent to query MCP tools incorrectly or inconsistently

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Other Parameters

Consider these additional parameter recommendations:
  • Max tokens: Set appropriately for your use case - we recommend not limiting the number of tokens.
  • Top-p: If used, keep at 0.9 or lower for consistent behavior

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Prompt Engineering Best Practices

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Tool Calling Instructions

  1. Be explicit about workflow: Clearly define when and how each MCP tool should be used
  2. Handle errors gracefully: Provide clear instructions on error handling and fallback behavior

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Response Formatting

  1. Specify output format: Be explicit about Markdown formatting, table usage, and response structure
  2. User experience: Include guidelines on when to wait for user input vs. proceeding automatically

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Testing and Iteration

Validation Steps
  1. Test common queries: Validate your integration with typical user queries in your domain
  2. Edge case testing: Test with complex, multi-filter queries
  3. Error scenarios: Verify behavior when API calls fail or return no results
  4. Consistency checks: Run the same query multiple times to ensure consistent behavior
Performance Optimization
  1. Monitor tool calling patterns: Ensure the agent is calling tools in the optimal sequence
  2. Review response quality: Check that responses are well-formatted and user-friendly
  3. Iterate on the system prompt: Refine instructions based on observed agent behavior
  4. A/B test variations: Try different prompt formulations to find what works best for your use case

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Common Pitfalls to Avoid

  • ❌ Using high temperature settings (> 0.1)
  • ❌ Exposing internal function names in user-facing responses
  • ❌ Insufficient error handling instructions
  • ❌ Vague or ambiguous workflow instructions

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Need Help?

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Resources

AgentSource MCP API Reference

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Support

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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.