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AgentHotspot

AgentHotspot

Local
github.com/AgentHotspotDeveloper Tools3Updated 40w ago

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

  1. 01Search connectors with natural language
  2. 02Access the AgentHotspot catalog
  3. 03Connect through MCP-compatible clients

Use cases

  1. 01Find connectors relevant to an agent task
  2. 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.

mcp.json · Universal config
{
"mcpServers": {
"agenthotspot-agenthotspot": {
"command": "uvx",
"args": [
"agenthotspot-mcp"
]
}
}
}
Run commanduvx agenthotspot-mcp

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
pypiagenthotspot-mcp1.0.2

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

AgentHotspot Logo

AgentHotspot MCP Server

🔍 Search 6,000+ MCP connectors directly from your AI agent

AgentHotspot License Python MCP

Features • Quick Start • Installation • Usage • Contributing

agenthotspot.com


🌟 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.

  1. Fork the repository
  2. Create a feature branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'Add amazing feature')
  4. Push to the branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

📄 License

This project is licensed under the MIT License — see the LICENSE file for details.


🔗 Links


Built with ❤️ by the AgentHotspot team

Discover Connectors

Comments

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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.