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Explorium AgentSource MCP Server

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by explorium-ai

Explorium Business Data Hub

Discover companies, contacts, and business insights—powered by dozens of trusted external data sources.

This repository contains the configuration and setup files for connecting to Explorium's Model Context Protocol (MCP) server, enabling AI tools to access comprehensive business intelligence data.

Overview

The Explorium Business Data Hub provides AI tools with access to:

  • Company Search & Enrichment: Find companies by name, domain, or attributes with detailed firmographics
  • Contact Discovery: Locate and enrich professional contact information
  • Business Intelligence: Access technology stack, funding history, growth signals, and business events
  • Real-Time Data: Up-to-date information from dozens of trusted external data sources
  • Workflow Integration: Seamlessly integrate business data into AI-powered workflows

Search any company or professional for everything from emails and phone numbers to roles, growth signals, tech stack, business events, website changes, and more. Find qualified leads, research prospects, identify talent, or craft personalized outreach—all without leaving your AI tool.

Installation

Remote Server Connection

Open Claude Desktop and navigate to Settings > Connectors > Add Custom Connector. Enter the name as Explorium and the remote MCP server URL as https://mcp.explorium.ai/mcp.

Local Server Connection

Open Claude Desktop developer settings and edit your claude_desktop_config.json file to add the following configuration. See Claude Desktop MCP docs for more info.

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

Go to: Settings -> Cursor Settings -> MCP -> Add new global MCP server

Pasting the following configuration into your Cursor ~/.cursor/mcp.json file is the recommended approach. You may also install in a specific project by creating .cursor/mcp.json in your project folder. See Cursor MCP docs for more info.

Cursor Remote Server Connection
{ "mcpServers": { "explorium": { "url": "https://mcp.explorium.ai/mcp" } } }
Cursor Local Server Connection
{ "mcpServers": { "explorium": { "command": "npx", "args": ["-y", "mcp-remote", "https://mcp.explorium.ai/mcp"] } } }

Add this to your Windsurf MCP config file. See Windsurf MCP docs for more info.

Windsurf Remote Server Connection
{ "mcpServers": { "explorium": { "serverUrl": "https://mcp.explorium.ai/mcp" } } }
Windsurf Local Server Connection
{ "mcpServers": { "explorium": { "command": "npx", "args": ["-y", "mcp-remote", "https://mcp.explorium.ai/mcp"] } } }

Add this to your VS Code MCP config file. See VS Code MCP docs for more info.

VS Code Remote Server Connection
"mcp": { "servers": { "explorium": { "type": "http", "url": "https://mcp.explorium.ai/mcp" } } }
VS Code Local Server Connection
"mcp": { "servers": { "explorium": { "type": "stdio", "command": "npx", "args": ["-y", "mcp-remote", "https://mcp.explorium.ai/mcp"] } } }

It can be installed via Zed Extensions or you can add this to your Zed settings.json. See Zed Context Server docs for more info.

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

You can easily install Explorium through the Cline MCP Server Marketplace by following these instructions:

  1. Open Cline.
  2. Click the hamburger menu icon (☰) to enter the MCP Servers section.
  3. Use the search bar within the Marketplace tab to find Explorium.
  4. Click the Install button.

Add this to your Roo Code MCP configuration file. See Roo Code MCP docs for more info.

Roo Code Remote Server Connection
{ "mcpServers": { "explorium": { "type": "streamable-http", "url": "https://mcp.explorium.ai/mcp" } } }
Roo Code Local Server Connection
{ "mcpServers": { "explorium": { "command": "npx", "args": ["-y", "mcp-remote", "https://mcp.explorium.ai/mcp"] } } }

See Gemini CLI Configuration for details.

  1. Open the Gemini CLI settings file. The location is ~/.gemini/settings.json (where ~ is your home directory).
  2. Add the following to the mcpServers object in your settings.json file:
{ "mcpServers": { "explorium": { "httpUrl": "https://mcp.explorium.ai/mcp" } } }

Or, for a local server:

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

If the mcpServers object does not exist, create it.

See JetBrains AI Assistant Documentation for more details.

  1. In JetBrains IDEs go to Settings -> Tools -> AI Assistant -> Model Context Protocol (MCP)
  2. Click + Add.
  3. Click on Command in the top-left corner of the dialog and select the As JSON option from the list
  4. Add this configuration and click OK
{ "mcpServers": { "explorium": { "command": "npx", "args": ["-y", "mcp-remote", "https://mcp.explorium.ai/mcp"] } } }
  1. Click Apply to save changes.
  2. The same way explorium could be added for JetBrains Junie in Settings -> Tools -> Junie -> MCP Settings

See Kiro Model Context Protocol Documentation for details.

  1. Navigate Kiro > MCP Servers
  2. Add a new MCP server by clicking the + Add button.
  3. Paste the configuration given below:
{ "mcpServers": { "Explorium": { "command": "npx", "args": ["-y", "mcp-remote", "https://mcp.explorium.ai/mcp"], "env": {}, "disabled": false, "autoApprove": [] } } }
  1. Click Save to apply the changes.

Connecting to Explorium MCP

For advanced users or other MCP clients, you can connect using these methods:

You can connect your AI tool to Explorium using the Model Context Protocol (MCP) through several methods:

  • URL: https://mcp.explorium.ai/mcp
  • JSON config:
{ "mcpServers": { "Explorium": { "url": "https://mcp.explorium.ai/mcp" } } }

SSE (Server-Sent Events)

  • URL: https://mcp.explorium.ai/sse
  • JSON config:
{ "mcpServers": { "Explorium": { "url": "https://mcp.explorium.ai/sse" } } }

STDIO (Local Server)

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

API Key Requirements

Important: Different connection methods have different authentication requirements:

  • Claude Desktop Extension - No API key required
  • MCP Remote Connections (Streamable HTTP/SSE/STDIO) - No API key required
  • 🔑 Docker Self-Hosting - Requires API key

Getting Your API Key

For Docker deployment, you'll need an API access token. Get yours at: https://admin.explorium.ai/api-key

Docker Deployment

This repository includes Docker configuration for self-hosting:

# Build the Docker image docker build -t explorium-mcp . # Run the container with API access token docker run -e API_ACCESS_TOKEN=your_explorium_access_token explorium-mcp

Required Environment Variables:

  • API_ACCESS_TOKEN - Your Explorium API access token for authentication (get it here)

You can also use a .env file or docker-compose for easier management:

# docker-compose.yml version: '3.8' services: explorium-mcp: build: . ports: - "44280:44280" environment: - API_ACCESS_TOKEN=${API_ACCESS_TOKEN}

Available Tools

Once connected, your AI tool will have access to tools for:

  • Business Matching: Find companies by name, domain, or business ID
  • Business Enrichment: Get detailed firmographics, technographics, and business intelligence
  • Business Events: Track funding rounds, office changes, hiring trends, and company developments
  • Prospect Discovery: Search for professionals and contacts within companies
  • Prospect Enrichment: Access contact information, work history, and professional profiles
  • Prospect Events: Track role changes, company moves, and career milestones

Troubleshooting Connection Issues

If you're experiencing issues connecting your AI tool to Explorium MCP:

  1. Check MCP Client Support
    Verify that your AI tool supports MCP clients and can connect to MCP servers. Not all AI tools have this capability built-in yet.
  2. Verify Remote Server Support
    Some AI tools have MCP clients but don't support remote connections. If this is the case, you may still be able to connect using our Docker configuration or local server setup.
  3. Request MCP Support
    If your AI tool doesn't support MCP at all, we recommend reaching out to the tool's developers to request MCP server connection support.

Configuration Files

This repository contains:

  • package.json - Node.js dependencies and scripts
  • manifest.json - Extension metadata and configuration
  • Dockerfile - Container configuration for self-hosting
  • server/index.js - Placeholder file (does not contain actual MCP implementation)
  • entrypoint.sh - Docker container entry point

Important Note: The server/index.js file in this repository is just a placeholder and does not contain the actual MCP server implementation. To use Explorium MCP, you need to connect to the remote server at https://mcp.explorium.ai/mcp using mcp-remote or through the connection methods described above. The actual MCP server is hosted by Explorium and accessible via the remote URLs.

Documentation & Support

For technical support, contact support@explorium.ai.

License

This project is licensed under the MIT License. See LICENSE for details.


Deploy Server
A
security – no known vulnerabilities
A
license - permissive license
A
quality - confirmed to work

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

Explorium AgentSource MCP Server empowers every agent to become an AI-driven, Go-To-Market specialized agent! With over 20 specialized endpoints designed for prospecting, sales, and lead generation, agents can effortlessly generate and enrich accounts and prospects, access deep business insights, an

  1. 📋 Table of Contents
    1. Overview
      1. Installation
        1. Setup for Development
          1. Running Locally
            1. Usage with AI Assistants
              1. Usage with Claude Desktop
              2. Usage with Cursor
            2. Project Structure
              1. Development Workflow
                1. Continuous Integration
                  1. Building and Publishing
                    1. Building the Package
                    2. Publishing to PyPI
                    3. Automatic Versioning and Tagging

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