Agentset MCP Server

What does “Agentset” MCP Server do?

The Agentset MCP (Model Context Protocol) Server is an open-source platform designed to facilitate Retrieval-Augmented Generation (RAG) with agentic capabilities. It allows AI assistants to connect with external data sources, APIs, or services, streamlining the development of intelligent, document-based applications. By acting as a bridge between AI clients and context-rich resources, the Agentset MCP Server enables tasks such as dynamic document retrieval, efficient data management, and integration with custom workflows. This empowers developers to build robust, context-aware solutions with enhanced productivity and flexibility, leveraging both AI and real-world data sources for advanced application scenarios.

List of Prompts

No prompt templates are explicitly mentioned in the available documentation or repository files.

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List of Resources

No specific resources (MCP Resources) are enumerated in the available documentation or repository files.

List of Tools

No explicit tools are listed or described in the available documentation or repository files (e.g., server.py not present or no tool list in README).

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Use Cases of this MCP Server

• Retrieval-Augmented Generation (RAG): Rapidly build applications that combine AI-generated responses with context retrieved from documents or external sources, improving the relevance and accuracy of AI outputs.
• Document-Based Application Development: Streamline the creation of intelligent apps that can access, manage, and reason over large document sets.
• API and Data Source Integration: Serve as a bridge between AI clients and APIs or databases, enabling seamless access to diverse data for richer, more dynamic AI interactions.
• Custom Workflow Automation: Enhance developer workflows by integrating AI-driven automation with organization-specific resources and processes.
• Secure Context Sharing: Ensure contextual information and credentials (such as API keys and namespace IDs) are handled securely via environment variables.

How to set it up

Windsurf

1. Ensure you have Node.js installed.

2. Obtain your Agentset API key and namespace ID.

3. Locate your Windsurf configuration file.

4. Add the Agentset MCP Server configuration:

   {
     "mcpServers": {
       "agentset": {
         "command": "npx",
         "args": ["-y", "@agentset/mcp@latest"],
         "env": {
           "AGENTSET_API_KEY": "your-agentset-api-key",
           "AGENTSET_NAMESPACE_ID": "your-namespace-id"
         }
       }
     }
   }
   

5. Save the configuration and restart Windsurf.

6. Verify the setup by checking the MCP server connection in the Windsurf interface.

Claude

1. Ensure Node.js is installed.

2. Obtain your Agentset API key and namespace ID.

3. Locate your Claude configuration file.

4. Add the following JSON configuration:

   {
     "mcpServers": {
       "agentset": {
         "command": "npx",
         "args": ["-y", "@agentset/mcp@latest"],
         "env": {
           "AGENTSET_API_KEY": "agentset_xxx",
           "AGENTSET_NAMESPACE_ID": "ns_xxx"
         }
       }
     }
   }
   

5. Save and restart Claude.

6. Confirm the MCP server is running from Claude’s admin tools.

Cursor

1. Install Node.js if not present.

2. Acquire your Agentset API key and namespace ID.

3. Edit your Cursor configuration file.

4. Insert this snippet into the mcpServers section:

   {
     "mcpServers": {
       "agentset": {
         "command": "npx",
         "args": ["-y", "@agentset/mcp@latest"],
         "env": {
           "AGENTSET_API_KEY": "your-agentset-api-key",
           "AGENTSET_NAMESPACE_ID": "your-namespace-id"
         }
       }
     }
   }
   

5. Save changes and restart Cursor.

6. Test the connection to ensure it is active.

Cline

1. Make sure Node.js is available.

2. Secure your Agentset API key and namespace ID.

3. Open your Cline configuration file.

4. Add the Agentset MCP Server as follows:

   {
     "mcpServers": {
       "agentset": {
         "command": "npx",
         "args": ["-y", "@agentset/mcp@latest"],
         "env": {
           "AGENTSET_API_KEY": "your-agentset-api-key",
           "AGENTSET_NAMESPACE_ID": "your-namespace-id"
         }
       }
     }
   }
   

5. Save and restart Cline.

6. Verify the connection in Cline’s system panel.

Securing API Keys Note:
Always use environment variables for sensitive information like AGENTSET_API_KEY and AGENTSET_NAMESPACE_ID.
Example:

"env": {
  "AGENTSET_API_KEY": "your-agentset-api-key",
  "AGENTSET_NAMESPACE_ID": "your-namespace-id"
}

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:

{
  "MCP-name": {
    "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 “MCP-name” to whatever the actual name of your MCP server is (e.g., “github-mcp”, “weather-api”, etc.) and replace the URL with your own MCP server URL.

Overview

Our opinion

The Agentset MCP Server repo provides a clear overview, setup instructions, and security guidance, but lacks detailed documentation on prompts, resources, and tools. While it is solid for application setup, it is limited in terms of feature and usage transparency.

MCP Score

Based on the two tables, the Agentset MCP Server currently scores a 4/10 for MCP readiness. It provides a strong foundation and basic setup but lacks the documentation and explicit feature exposure (prompts, tools, resources) necessary for full MCP utilization and evaluation.