About AgentHotspot
AgentHotspot MCP Server lets AI agents search the AgentHotspot marketplace for MCP connectors using natural-language queries. It is intended for agent developers seeking connectors from the catalog.
Key features
- 01Search connectors with natural language
- 02Access the AgentHotspot catalog
- 03Connect through MCP-compatible clients
Use cases
- 01Find connectors relevant to an agent task
- 02Discover available MCP integrations during development
MCP support
The server exposes AgentHotspot marketplace connector search to MCP clients.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEAgentHotspot MCP Server
🔍 Search 6,000+ MCP connectors directly from your AI agent
Features • Quick Start • Installation • Usage • Contributing
🌟 What is AgentHotspot?
AgentHotspot a marketplace for AI agent developers. It provides:
- 🔌 6,000+ curated MCP connectors ready to connect and integrate for agent builders
- 🚀 One-click integration with Claude Desktop, OpenAI Agents, n8n, and more
- 💰 Instant Monetization tools for MCP connector creators
- 📊 Analytics dashboard to track usage and performance
This MCP server allows your AI agents to search and discover oss connectors from the AgentHotspot marketplace.
✨ Features
- 🔍 Search Connectors — Query the AgentHotspot catalog with natural language
- 📦 Lightweight — Minimal dependencies, easy to install
- 🔧 MCP Compatible — Works with any MCP-compatible client
📦 Installation
Prerequisites
- Python 3.10+
- An MCP-compatible client (Claude Desktop, OpenAI Agents SDK, custom agents, etc.)
From Source
git clone https://github.com/AgentHotspot/agenthotspot-mcp.git
cd agenthotspot-mcp
# Install dependencies
pip install -r requirements.txt
# Install module
pip install -e .
🔧 Usage
Run the Server Independently
# Run directly
python3 -m agenthotspot_mcp
# Or using the script
python3 src/agenthotspot_mcp/server.py
With Claude Desktop
Add this configuration to your Claude Desktop config file:
macOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"agenthotspot": {
"command": "python3",
"args": ["-m", "agenthotspot_mcp"]
}
}
}
With LangChain
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
async def main():
client = MultiServerMCPClient({
"agenthotspot": {
"transport": "stdio",
"command": "python3",
"args": ["-m", "agenthotspot_mcp"],
}
})
tools = await client.get_tools()
print(tools)
# Remaining code ...
# (see examples/langchain_example.py for full agent example)
asyncio.run(main())
🗂️ Project Structure
agenthotspot-mcp/
├── src/
│ └── agenthotspot_mcp/
│ ├── __init__.py # Package exports
│ ├── __main__.py # Entry point
│ └── server.py # MCP server implementation
├── examples/
│ ├── claude_config.json # Claude Desktop config example
│ └── langchain_example.py # Python langchain usage example
├── pyproject.toml # Package configuration
├── requirements.txt # Dependencies
├── LICENSE # MIT License
├── CONTRIBUTING.md # Contribution guidelines
└── README.md # This file
🤝 Contributing
We welcome contributions! See CONTRIBUTING.md for guidelines.
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
📄 License
This project is licensed under the MIT License — see the LICENSE file for details.
🔗 Links
- 🌐 Website: agenthotspot.com
- 📦 Connectors: Browse 6,000+ connectors
- 🐦 Twitter/X: @agenthotspot
- 🐙 GitHub: AgentHotspot
- 📧 Support: [email protected]
Built with ❤️ by the AgentHotspot team
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.