Explorium MCP Server: Company and Contact Insights

Overview

The Explorium MCP Server exposes a Model Context Protocol (MCP) endpoint that provides consolidated company and contact intelligence aggregated from dozens of external sources. It lets AI assistants and developer tools query firmographics, contact records, technology signals, funding history, business events, website changes, and other enrichment attributes without building custom data pipelines.

Designed to be used as an MCP-compatible context server, Explorium integrates with LLM-powered workflows (Claude, VS Code Copilot Chat, Cursor, Zed, Windsurf, etc.). Developers can attach this server to their local or hosted MCP client and surface structured business data directly into prompts and chains to improve prospecting, research, personalization, or internal analytics.

Features

• Company search by name, domain, industry, or attributes
• Company enrichment: firmographics, revenue, headcount, headquarters, funding rounds
• Contact discovery and enrichment: roles, emails, phone numbers, social profiles
• Technology stack signals and third‑party integrations
• Business events and growth signals: hires, funding, product launches, website changes
• Real‑time data aggregation from many trusted external sources
• MCP compatibility for easy integration with LLM assistants and developer tools
• Node/TypeScript configuration files and local remote wrapper support (mcp-remote)

Installation / Configuration

Below are example configurations for common MCP clients. Use the remote HTTP URL (https://mcp.explorium.ai/mcp) for a hosted connection, or run a local proxy with mcp-remote for stdio/command integrations.

Quick notes:

• The repository is TypeScript-powered and targets modern Node.js (v24+ recommended).
• You can connect via HTTP or run a local wrapper with the mcp-remote helper (npx mcp-remote ).

  Claude Desktop (remote)

  {
    "mcpServers": {
      "explorium": {
        "command": "npx",
        "args": ["-y", "mcp-remote", "https://mcp.explorium.ai/mcp"]
      }
    }
  }
  

  Cursor (remote)

  {
    "mcpServers": {
      "explorium": {
        "url": "https://mcp.explorium.ai/mcp"
      }
    }
  }
  

  VS Code Copilot Chat (remote HTTP)

  "mcp": {
    "servers": {
      "explorium": {
        "type": "http",
        "url": "https://mcp.explorium.ai/mcp"
      }
    }
  }
  

  VS Code Copilot Chat (stdio/local wrapper)

  "mcp": {
    "servers": {
      "explorium": {
        "type": "stdio",
        "command": "npx",
        "args": ["-y", "mcp-remote", "https://mcp.explorium.ai/mcp"]
      }
    }
  }
  

  Zed (local wrapper via npx)

  {
    "context_servers": {
      "Explorium": {
        "command": {
          "path": "npx",
          "args": ["-y", "mcp-remote", "https://mcp.explorium.ai/mcp"]
        },
        "settings": {}
      }
    }
  }
  

  Windsurf (remote)

  {
    "mcpServers": {
      "explorium": {
        "serverUrl": "https://mcp.explorium.ai/mcp"
      }
    }
  }
  

  If you prefer a local bridge, install the mcp-remote helper and point your client at the generated stdio command. See the repository for examples and bootstrapping scripts.

  Available Resources

  The Explorium MCP Server exposes a set of business data resources that LLMs and tools can query as part of prompt context. Typical resource categories include:

  • Company Search: match by name, domain, keywords
  • Company Enrichment: firmographic attributes, size, revenue, sector, funding history
  • Contact Search: role/title-based discovery, verified emails, phone numbers
  • Contact Enrichment: contact-level attributes and public profiles
  • Technology Signals: detected tech stack, integrations, trackers
  • Events & Signals: funding, hires, product launches, website changes
  • Raw Source Attribution: provenance and timestamps for returned facts

  Responses are returned as structured JSON suitable for embedding into prompts or downstream processing.

  Use Cases

  • Prospecting: find decision-makers at target accounts with enriched contact details and organizational context to personalize outreach. Example prompt to an LLM:

    Find senior product leaders at "Acme Inc", return names, titles, emails and recent product announcements.
    

  • Market research: gather firmographics and funding history across competitors for a landscape report.

    Compare revenue, headcount, and most recent funding round for companies in the cybersecurity sector.
    

  • Deal intelligence: surface recent website changes, hires, or funding signals that indicate a buying opportunity.

    Alert me when a prospect receives Series A funding or posts a hiring surge in engineering.
    

  • Recruitment and sourcing: identify engineering leaders and their public profiles for outreach.

    Get the engineering leadership team at "Palo Alto Networks" with LinkedIn and email addresses.
    

  Getting Started

  1. Pick your client (Claude Desktop, Cursor, VS Code, Zed, etc.).
  2. Add the Explorium MCP server configuration (remote URL or local wrapper).
  3. Use your LLM prompts to call the relevant resources (company/contact search/enrich).
  4. Parse structured JSON responses into your app or include them directly in LLM context.

  Repository and source code: https://github.com/explorium-ai/mcp-explorium

  For deeper integration details and example queries, consult the repository README and MCP client docs for your environment.

  Tags:aiai-mlb2bcompany datacompany-datacontact datacontact-datadataenrichmentsllmmcpprospects