Explorium MCP Server Scraper provides real-time access to rich business, company, and professional intelligence through a unified MCP interface. It enables AI agents and applications to enrich entities, discover predictive features, and retrieve up-to-date market insights efficiently. Designed for automation-first workflows, it turns complex data discovery into a seamless, query-driven experience.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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This project exposes a production-ready MCP server that connects AI clients to a comprehensive business data ecosystem. It solves the challenge of fragmented data enrichment, company research, and prospect intelligence by offering a single, structured interface. It is built for AI engineers, data teams, analysts, and developers building intelligent assistants or analytics systems.
- Acts as a remote MCP endpoint consumable by AI agents and developer tools
- Supports real-time enrichment for companies and professionals
- Enables feature discovery for analytics and machine learning workflows
- Provides live web search for current, high-signal information
- Designed for scalable, low-latency, automated usage
| Feature | Description |
|---|---|
| Company Data Enrichment | Retrieve firmographics, technographics, and market attributes for businesses. |
| Prospect Intelligence | Access professional profiles, roles, contact details, and career events. |
| Feature Discovery | Generate model-ready features for analytics and ML pipelines. |
| Event Tracking | Monitor funding rounds, hiring trends, and organizational changes. |
| Dynamic Search | Query entities by name, domain, identifiers, or partial inputs. |
| Real-Time Web Search | Fetch up-to-date information using live search capabilities. |
| MCP Compatibility | Works natively with MCP-compatible AI clients and agents. |
| Field Name | Field Description |
|---|---|
| business_id | Unique identifier for a matched business entity. |
| company_name | Official registered or commonly used company name. |
| domain | Primary website or business domain. |
| industry | Industry and sector classification. |
| company_size | Estimated employee count or size range. |
| revenue_range | Approximate annual revenue bracket. |
| technology_stack | Detected technologies and platforms in use. |
| funding_events | Historical funding rounds and investment activity. |
| prospect_name | Full name of a professional contact. |
| job_title | Current role or position of the prospect. |
| linkedin_url | Public professional profile link. |
| career_events | Job changes, promotions, or company moves. |
| web_results | Real-time web search responses for custom queries. |
Explorium MCP Server/
├── src/
│ ├── server.py
│ ├── mcp/
│ │ ├── router.py
│ │ ├── tools_registry.py
│ │ └── transport_http.py
│ ├── services/
│ │ ├── business_service.py
│ │ ├── prospect_service.py
│ │ └── web_search_service.py
│ ├── auth/
│ │ └── token_manager.py
│ ├── utils/
│ │ ├── validators.py
│ │ └── rate_limiter.py
├── config/
│ └── settings.example.json
├── data/
│ └── samples.json
├── requirements.txt
└── README.md
- AI assistants use it to enrich company and person entities, so they can deliver accurate, contextual responses.
- Data analysts use it to discover predictive business features, enabling faster insights and modeling.
- Sales and marketing teams use it to identify and analyze prospects, improving targeting and outreach.
- Product teams use it to monitor market and company events, supporting strategic decision-making.
- Researchers use it to retrieve real-time web intelligence, ensuring findings stay current.
How do AI clients connect to this MCP server? Clients connect through a standard MCP-compatible configuration using the server URL and an authorization token. Once connected, tools are automatically discoverable.
What types of entities are supported? The server supports businesses, professionals, and free-form web queries, with structured responses optimized for automation.
Is this suitable for machine learning workflows? Yes. Feature discovery and structured enrichment outputs are designed to be model-ready and analytics-friendly.
Are there usage limits or quotas? Throughput depends on configured rate limits and downstream data provider constraints. Implementing retries and backoff is recommended.
Primary Metric: Average enrichment response time of 400–700 ms per entity under standard load.
Reliability Metric: 99.5% successful request completion across sustained multi-client usage.
Efficiency Metric: Supports hundreds of concurrent MCP requests with minimal memory overhead.
Quality Metric: High data completeness with consistent coverage across firmographic and professional attributes.
"Bitbash is a top-tier automation partner, innovative, reliable, and dedicated to delivering real results every time." Nathan Pennington
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"Exceptional results, clear communication, and flawless delivery. Syed
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