# MCP Client Configuration Examples

This document provides configuration examples for connecting various MCP clients to the hf-eda-mcp server.

## Table of Contents

- [Kiro IDE](#kiro-ide)
- [Claude Desktop](#claude-desktop)
- [Custom MCP Client](#custom-mcp-client)
- [Environment Variables](#environment-variables)

---

## Kiro IDE

### Workspace Configuration

Create or edit `.kiro/settings/mcp.json` in your workspace:

```json
{
  "mcpServers": {
    "hf-eda-mcp": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "-p", "7860:7860",
        "--env-file", ".env",
        "hf-eda-mcp:latest"
      ],
      "env": {
        "HF_TOKEN": "${HF_TOKEN}"
      },
      "disabled": false,
      "autoApprove": [
        "get_dataset_metadata",
        "get_dataset_sample",
        "analyze_dataset_features"
      ]
    }
  }
}
```

### User-Level Configuration

Edit `~/.kiro/settings/mcp.json` for global configuration:

```json
{
  "mcpServers": {
    "hf-eda-mcp": {
      "command": "pdm",
      "args": ["run", "hf-eda-mcp"],
      "env": {
        "HF_TOKEN": "your_token_here"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}
```

### Using HuggingFace Spaces

```json
{
  "mcpServers": {
    "hf-eda-mcp": {
      "url": "https://your-username-hf-eda-mcp.hf.space/gradio_api/mcp/sse",
      "disabled": false,
      "autoApprove": ["get_dataset_metadata"]
    }
  }
}
```

---

## Claude Desktop

### Configuration File Location

- **macOS**: `~/Library/Application Support/Claude/claude_desktop_config.json`
- **Windows**: `%APPDATA%\Claude\claude_desktop_config.json`
- **Linux**: `~/.config/Claude/claude_desktop_config.json`

### Local Server Configuration

```json
{
  "mcpServers": {
    "hf-eda-mcp": {
      "command": "python",
      "args": ["-m", "hf_eda_mcp"],
      "env": {
        "HF_TOKEN": "your_token_here",
        "PYTHONPATH": "/path/to/hf-eda-mcp/src"
      }
    }
  }
}
```

### Docker Configuration

```json
{
  "mcpServers": {
    "hf-eda-mcp": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "-p", "7860:7860",
        "-e", "HF_TOKEN=your_token_here",
        "hf-eda-mcp:latest"
      ]
    }
  }
}
```

### HuggingFace Spaces Configuration

```json
{
  "mcpServers": {
    "hf-eda-mcp": {
      "url": "https://your-username-hf-eda-mcp.hf.space/gradio_api/mcp/sse"
    }
  }
}
```

---

## Custom MCP Client

### Python Client Example

```python
import asyncio
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

async def main():
    # Connect to local server
    server_params = StdioServerParameters(
        command="python",
        args=["-m", "hf_eda_mcp"],
        env={"HF_TOKEN": "your_token_here"}
    )
    
    async with stdio_client(server_params) as (read, write):
        async with ClientSession(read, write) as session:
            # Initialize the connection
            await session.initialize()
            
            # List available tools
            tools = await session.list_tools()
            print("Available tools:", tools)
            
            # Call a tool
            result = await session.call_tool(
                "get_dataset_metadata",
                arguments={"dataset_id": "squad"}
            )
            print("Result:", result)

if __name__ == "__main__":
    asyncio.run(main())
```

### JavaScript/TypeScript Client Example

```typescript
import { Client } from "@modelcontextprotocol/sdk/client/index.js";
import { StdioClientTransport } from "@modelcontextprotocol/sdk/client/stdio.js";

async function main() {
  const transport = new StdioClientTransport({
    command: "python",
    args: ["-m", "hf_eda_mcp"],
    env: {
      HF_TOKEN: process.env.HF_TOKEN
    }
  });

  const client = new Client({
    name: "hf-eda-client",
    version: "1.0.0"
  }, {
    capabilities: {}
  });

  await client.connect(transport);

  // List tools
  const tools = await client.listTools();
  console.log("Available tools:", tools);

  // Call a tool
  const result = await client.callTool({
    name: "get_dataset_metadata",
    arguments: {
      dataset_id: "squad"
    }
  });
  console.log("Result:", result);

  await client.close();
}

main().catch(console.error);
```

---

## Environment Variables

### Required Variables

- `HF_TOKEN`: HuggingFace API token (optional for public datasets, required for private datasets)

### Optional Variables

- `HF_HOME`: Directory for HuggingFace cache (default: `~/.cache/huggingface`)
- `HF_DATASETS_CACHE`: Directory for datasets cache
- `TRANSFORMERS_CACHE`: Directory for transformers cache
- `GRADIO_SERVER_NAME`: Server host (default: `0.0.0.0`)
- `GRADIO_SERVER_PORT`: Server port (default: `7860`)
- `MCP_SERVER_ENABLED`: Enable MCP server (default: `true`)

### Example .env File

```bash
# HuggingFace Authentication
HF_TOKEN=hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxx

# Cache Configuration
HF_HOME=/path/to/cache
HF_DATASETS_CACHE=/path/to/cache/datasets
TRANSFORMERS_CACHE=/path/to/cache/transformers

# Server Configuration
GRADIO_SERVER_NAME=0.0.0.0
GRADIO_SERVER_PORT=7860
MCP_SERVER_ENABLED=true
```

---

## Deployment Options Comparison

| Option | Pros | Cons | Best For |
|--------|------|------|----------|
| **Local (PDM)** | Fast, easy debugging | Requires Python setup | Development |
| **Docker** | Isolated, reproducible | Requires Docker | Production, CI/CD |
| **HF Spaces** | Hosted, no maintenance | Limited control | Public sharing |

---

## Troubleshooting

### Connection Issues

1. **Server not starting**: Check logs for errors, verify dependencies installed
2. **Authentication failed**: Verify `HF_TOKEN` is set correctly
3. **Port already in use**: Change `GRADIO_SERVER_PORT` to a different port

### Tool Execution Issues

1. **Dataset not found**: Verify dataset ID is correct on HuggingFace Hub
2. **Permission denied**: Ensure `HF_TOKEN` has access to private datasets
3. **Timeout errors**: Increase timeout settings or use smaller sample sizes

### Docker Issues

1. **Image build fails**: Ensure all dependencies in `pyproject.toml` are compatible
2. **Container exits immediately**: Check logs with `docker logs hf-eda-mcp-server`
3. **Cache not persisting**: Verify volume mounts in `docker-compose.yml`

---

## Additional Resources

- [MCP Protocol Documentation](https://modelcontextprotocol.io/)
- [Gradio MCP Integration](https://www.gradio.app/guides/gradio-and-mcp)
- [HuggingFace Hub Documentation](https://huggingface.co/docs/hub/index)
- [Project Repository](https://github.com/your-username/hf-eda-mcp)