AI Agent Marketplace Index MCP Server

What does “AI Agent Marketplace Index” MCP Server do?

The AI Agent Marketplace Index MCP Server is a specialized Model Context Protocol (MCP) server developed by DeepNLP to provide AI assistants with seamless access to a comprehensive index of AI agents. This server enables AI-powered tools and assistants to search and discover available AI agents based on keywords or categories, such as “AI coding agents,” “Healthcare AI Agents,” or “Mobile Use Agent.” Additionally, it offers features to monitor web traffic performance for these agents, including metrics like Google/Bing rankings and GitHub stars, and provides APIs for listing new AI agents into the marketplace. By integrating with this MCP server, developers can enhance their workflow with advanced search, categorization, and monitoring capabilities for AI agents, facilitating more efficient development, research, and deployment of AI solutions.

List of Prompts

No explicit prompt templates are mentioned in the repository or documentation.

List of Resources

No explicit list of resources as MCP “resources” is provided in the repository or documentation.

List of Tools

• Search AI Agents by Query or Category
  Enables LLMs and clients to search for AI agents using specific keywords or categories, returning a list of available agents from the marketplace index.
• Monitor AI Agents Web Traffic Performance
  Allows retrieval of web performance data for AI agents, including metrics such as Google/Bing rankings and GitHub stars.
• API to List AI Agents
  Provides the functionality to register or list new AI agents into the marketplace index.
• Comprehensive Error Handling
  Built-in error handling for robust and reliable operations.

Use Cases of this MCP Server

• Discovering Relevant AI Agents
  Developers and researchers can quickly search for AI agents by category or keyword, streamlining the process of finding tools suited for specific tasks or industries (e.g., coding, HR, finance).
• Monitoring AI Agent Popularity and Performance
  By accessing web traffic and ranking data, users can assess the popularity and impact of various AI agents to make informed adoption decisions.
• Integrating AI Agent Discovery into Custom Workflows
  Teams can embed marketplace search capabilities into their own AI-powered products or dashboards.
• Promoting New AI Agents
  Developers can easily list and promote their AI agents to a wider audience through the marketplace.
• Aggregating Use Cases and Functionalities
  Facilitates research and analysis by aggregating available agent functionalities and use cases in one searchable index.

How to set it up

Windsurf

1. Ensure Python 3.10+ is installed on your system.

2. Install the MCP server per the repository’s Installation instructions.

3. Open Windsurf’s configuration file (e.g., windsurf.json).

4. Add the AI Agent Marketplace Index MCP server to the mcpServers section:

   {
     "mcpServers": {
       "ai-agent-marketplace-index": {
         "command": "python",
         "args": ["main.py"]
       }
     }
   }
   

5. Save and restart Windsurf.

6. Verify the MCP server is connected by searching for AI agents within Windsurf.

Securing API Keys Example:

{
  "env": {"BING_SEARCH_API_KEY": "your_api_key"},
  "inputs": {}
}

Claude

1. Set up Python 3.10+ and install the MCP server dependencies.

2. Locate Claude’s configuration file.

3. Add the following MCP server configuration:

   {
     "mcpServers": {
       "ai-agent-marketplace-index": {
         "command": "python",
         "args": ["main.py"]
       }
     }
   }
   

4. Save and restart Claude.

5. Confirm the server is available as a tool in Claude.

Securing API Keys Example:

{
  "env": {"BING_SEARCH_API_KEY": "your_api_key"},
  "inputs": {}
}

Cursor

1. Install Python 3.10+ and clone/install the MCP server.

2. Open Cursor’s MCP configuration file.

3. Add the AI Agent Marketplace Index MCP server:

   {
     "mcpServers": {
       "ai-agent-marketplace-index": {
         "command": "python",
         "args": ["main.py"]
       }
     }
   }
   

4. Save the configuration and restart Cursor.

5. Verify by searching for AI agents from within Cursor.

Securing API Keys Example:

{
  "env": {"BING_SEARCH_API_KEY": "your_api_key"},
  "inputs": {}
}

Cline

1. Ensure Python 3.10+ is installed and MCP server is set up.

2. Edit Cline’s configuration file.

3. Add the MCP server entry:

   {
     "mcpServers": {
       "ai-agent-marketplace-index": {
         "command": "python",
         "args": ["main.py"]
       }
     }
   }
   

4. Save and restart Cline.

5. Confirm the AI Agent Marketplace Index MCP is available.

Securing API Keys Example:

{
  "env": {"BING_SEARCH_API_KEY": "your_api_key"},
  "inputs": {}
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "ai-agent-marketplace-index": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “ai-agent-marketplace-index” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Based on the above checks, this MCP is functional and well-integrated for its purpose but lacks explicit prompt and resource definitions. Tooling and setup are clear, but advanced MCP features like sampling and roots are not documented.

Rating:
I’d rate this MCP server a 6/10. It provides solid search and monitoring capabilities with clear setup instructions, but lacks explicit support for advanced MCP features and clear prompt/resource definitions.

MCP Score