mcp-hfspace MCP Server

What does “mcp-hfspace” MCP Server do?

The mcp-hfspace MCP Server is designed to connect AI assistants with HuggingFace Spaces—external AI models, demos, and APIs hosted on HuggingFace. This server acts as a bridge, enabling AI agents and developers to interact with, query, and manage HuggingFace Spaces programmatically. By exposing endpoints and configurable workflows, mcp-hfspace enhances development workflows for those integrating AI features, such as running ML models or demos, into their applications. It allows automation of tasks like invoking models, retrieving outputs, and managing data exchange, significantly simplifying access to a vast ecosystem of pretrained AI tools and APIs.

List of Prompts

No information about prompt templates is provided in the repository or documentation.

List of Resources

No explicit resources are listed or described in the repository or its documentation.

List of Tools

No detailed list of tools (such as those defined in a server.py or otherwise) is available from the accessible files or documentation.

Use Cases of this MCP Server

• Access HuggingFace Spaces
  Seamlessly invoke any public HuggingFace Space, allowing developers to leverage a wide variety of AI demos, models, and applications directly from their own workflow or application.
• Integrate AI Models into Apps
  Use the MCP server to call external models for inference, making it easy to embed state-of-the-art AI tasks such as text generation, image classification, or audio processing.
• Automate Testing of AI Models
  Run automated scripts that interact with multiple HuggingFace Spaces to benchmark or validate outputs in a standardized way.
• Simplify Data Pipelining
  Use the server to orchestrate flows where data is passed to multiple Spaces and results are aggregated or further processed.
• Prototype with Claude Desktop Mode
  Leverage easy configuration and integration with Claude Desktop, allowing fast prototyping and local testing of AI-driven features.

How to set it up

Windsurf

1. Prerequisites: Ensure Node.js and Windsurf are installed.
2. Locate Configuration: Open your Windsurf configuration file (e.g., windsurf.json).
3. Add mcp-hfspace Server:

   "mcpServers": {
     "hfspace": {
       "command": "npx",
       "args": ["@evalstate/mcp-hfspace@latest"]
     }
   }
   

4. Save and Restart: Save your config and restart Windsurf.
5. Verify: Check that the server is listed and accessible in Windsurf.

Claude

1. Prerequisites: Make sure you have Claude Desktop installed.
2. Edit Configuration: Open the Claude configuration file.
3. Add mcp-hfspace:

   "mcpServers": {
     "hfspace": {
       "command": "npx",
       "args": ["@evalstate/mcp-hfspace@latest"]
     }
   }
   

4. Restart Claude: Save changes and restart.
5. Verify: Confirm server registration in the Claude interface.

Cursor

1. Prerequisites: Install Cursor with MCP plugin capability.
2. Open config file: Edit your Cursor configuration.
3. Configure Server:

   "mcpServers": {
     "hfspace": {
       "command": "npx",
       "args": ["@evalstate/mcp-hfspace@latest"]
     }
   }
   

4. Save and Relaunch: Restart Cursor.
5. Check: Ensure hfspace shows up as an available MCP server.

Cline

1. Prerequisites: Install Cline and Node.js.
2. Edit Cline config: Open the config file (e.g., cline.json).
3. Insert mcp-hfspace:

   "mcpServers": {
     "hfspace": {
       "command": "npx",
       "args": ["@evalstate/mcp-hfspace@latest"]
     }
   }
   

4. Restart Cline: Save and restart the tool.
5. Confirm: Verify integration by listing available servers.

Securing API Keys

You should secure HuggingFace API keys by using environment variables. Example:

"mcpServers": {
  "hfspace": {
    "command": "npx",
    "args": ["@evalstate/mcp-hfspace@latest"],
    "env": {
      "HF_API_KEY": "your_huggingface_api_key"
    },
    "inputs": {
      "apiKey": "${HF_API_KEY}"
    }
  }
}

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:

{
  "hfspace": {
    "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 “hfspace” 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, the mcp-hfspace MCP server offers basic integration and setup support, but lacks documentation on prompts, resources, and tools. Its main strength is clear setup for several platforms and credential management. I would rate this MCP server a 4/10 for documentation and developer-friendliness.

MCP Score