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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.

Claude by Anthropic - The most popular MCP-compatible AI assistant Claude Desktop provides native support for MCP servers, making it the ideal choice for accessing AgentSource data through natural conversation.

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Setup Instructions

1

Install Claude Desktop

Install Claude Desktop from claude.ai
2

Click on "Search and tools" button

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3

Click on the "Add connectors" option

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4

Go to "Desktop extensions" tab

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5

Select "Explorium business data hub" from the list of available extensions

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6

Click on the Install button and wait for the installation process to complete

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7

Verify that the Explorium business data hub is enabled now and close the extension window

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8

Again click on the 'Search and tools' button to verify that Explorium business data hub appears and enable it by clicking on the toggle to the right of the extension name

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9

Allow Explorium MCP access

  • Allow Explorium MCP access
MCP Access Permission

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Example Conversations

Finding Companies: 
You: Find me 10 SaaS companies in San Francisco with 50-200 employees

Claude: I'll search for SaaS companies in San Francisco with 50-200 employees.

[Searches using fetch_businesses tool]

I found 10 SaaS companies matching your criteria:

1. TechFlow Analytics
   - 125 employees
   - Revenue: $10M-$25M
   - Technologies: AWS, React, PostgreSQL

2. DataSync Solutions
   - 87 employees
   - Revenue: $5M-$10M
   - Technologies: Google Cloud, Python, MongoDB

[... continues with detailed results]
Researching Contacts: 
You: Who is the CTO of Snowflake and what's their background?

Claude: I'll look up information about Snowflake's CTO.

[Uses match_businesses, then enrich_businesses_financial_metrics]

The CTO of Snowflake is Benoit Dageville. Here's his background:
- Co-founder of Snowflake
- Previously at Oracle for 16 years
- PhD in Computer Science from University of Paris
- Expertise in database systems and query optimization

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Common Patterns

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Research Assistant Pattern

Transform your AI into a business research assistant: 
1. User asks about a company or industry
2. AI uses match_businesses or fetch_businesses
3. AI enriches with firmographics, technographics, financials
4. AI summarizes findings in natural language

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Sales Intelligence Pattern

Enable your AI to support sales activities: 
1. User provides target company name
2. AI finds company with match_businesses
3. AI fetches key contacts with fetch_prospects
4. AI enriches contact information
5. AI provides actionable insights

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Market Analysis Pattern

Use AI for competitive intelligence: 
1. User asks about market segment
2. AI searches companies with filters
3. AI aggregates statistics
4. AI identifies trends and patterns
5. AI generates market report

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Best Practices

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Prompt Engineering

Be Specific 
Good: "Find B2B SaaS companies in NYC with 50-200 employees using AWS"
Bad: "Find some tech companies"
Chain Requests 
"First find Microsoft's business ID, then show me all VPs in their engineering department"
Request Formats 
"Show me the results as a table with company name, size, and revenue"

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Performance Tips

  • Batch Operations: Ask for multiple companies at once
  • Specific Filters: Use precise criteria to reduce results
  • Caching: AI assistants often cache recent results
  • Pagination: Request specific page numbers for large datasets

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Security Considerations

  • Store API keys in secure configuration files
  • Never share API keys in conversations
  • Use read-only keys when possible
  • Monitor usage through the admin panel

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Advanced Features

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Tool Chaining

Most AI assistants can automatically chain multiple tools: 
"Find the top 5 competitors of Salesforce and compare their employee counts and technologies"

This triggers:
1. match_businesses (Salesforce)
2. enrich_businesses_competitive_landscape
3. match_businesses (for each competitor)
4. enrich_businesses_firmographics
5. enrich_businesses_technographics

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Conversational Memory

AI assistants maintain context across messages: 
You: "Find info about Uber"
AI: [Provides Uber details]
You: "Now show me their main competitors"
AI: [Understands "their" refers to Uber]

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Natural Language Filters

Convert natural language to API filters: 
"Companies founded in the last 3 years" <Icon icon="arrow-right" /> company_age: ["0-3"]
"Mid-size businesses" <Icon icon="arrow-right" /> company_size: ["51-200", "201-500"]
"Tech companies" <Icon icon="arrow-right" /> linkedin_category: ["Technology"]

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Company Research

"Give me a complete profile of Tesla including their technology stack,
financial metrics, recent events, and key executives"

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Competitive Analysis

"Compare the top 5 CRM companies by employee count, revenue,
and technology stack. Show the results in a table."

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Lead Generation

"Find marketing directors at fintech companies in London
with 100-500 employees. Include their email addresses."

"What percentage of SaaS companies with $10M-$50M revenue
are using AWS vs Google Cloud vs Azure?"