switch to fast mcp implementation

.python-version

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+3.11

README.md

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----
-title: Inference Providers Mcp
-emoji: 😻
-colorFrom: gray
-colorTo: purple
-sdk: gradio
-sdk_version: 5.34.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# ⚡ Inference Providers FastMCP Server
+
+A **FastMCP Server** for Hugging Face Inference Providers, built with [FastMCP](https://github.com/jlowin/fastmcp) - the fast, Pythonic way to build MCP servers. This allows LLMs and AI assistants to access multiple AI providers and language models through the Model Context Protocol.
+
+## ✨ Features
+
+- **⚡ FastMCP**: Built with FastMCP for optimal performance and simplicity
+- **🚀 UV-Powered**: Uses UV/UVX for fast, modern Python dependency management
+- **🤖 MCP Server**: Native MCP server with tools, resources, and prompts
+- **🎯 Multi-Provider Support**: Access 14+ inference providers including Cerebras, Cohere, Fal AI, Fireworks, Groq, and more
+- **💬 Chat Completion**: Interactive conversations with LLMs and Vision Language Models
+- **📊 Resources**: Access provider information and popular model recommendations
+- **🔍 Context Logging**: Rich logging and error handling through MCP context
+- **🔧 Easy Integration**: Simple configuration for Cursor, Claude Desktop, and other MCP clients
+
+## 🚀 Supported Providers
+
+| Provider | Chat Completion | Vision Language Models |
+|----------|----------------|------------------------|
+| Cerebras | ✅ | ❌ |
+| Cohere | ✅ | ✅ |
+| Fal AI | ✅ | ✅ |
+| Featherless AI | ✅ | ✅ |
+| Fireworks | ✅ | ✅ |
+| Groq | ✅ | ❌ |
+| HF Inference | ✅ | ✅ |
+| Hyperbolic | ✅ | ✅ |
+| Nebius | ✅ | ✅ |
+| Novita | ✅ | ✅ |
+| Nscale | ✅ | ✅ |
+| Replicate | ✅ | ✅ |
+| SambaNova | ✅ | ✅ |
+| Together | ✅ | ✅ |
+
+## 🛠️ Quick Start
+
+### 1. Get a Hugging Face Token
+
+1. Go to [https://huggingface.co/settings/tokens](https://huggingface.co/settings/tokens)
+2. Create a new token with **Inference Providers** scope
+3. Copy the token (starts with `hf_`)
+
+### 2. Install Dependencies
+
+```bash
+# Clone the repository
+git clone <repository-url>
+cd inference-providers-mcp
+
+# Install dependencies
+pip install -r requirements.txt
+```
+
+### 3. Set Environment Variables
+
+Create a `.env` file in your project directory:
+
+```bash
+# .env file
+HF_TOKEN=hf_your_actual_token_here
+```
+
+Or set it globally:
+
+```bash
+# Linux/macOS
+export HF_TOKEN=hf_your_actual_token_here
+
+# Windows
+set HF_TOKEN=hf_your_actual_token_here
+```
+
+### 4. Test the Server
+
+```bash
+# Test the server works (using UV - recommended)
+uvx test_mcp.py
+
+# Or test with Python
+python test_mcp.py
+
+# Run the server manually (optional)
+uvx mcp_server.py
+# Or: python mcp_server.py
+```
+
+## 🎯 Cursor IDE Integration
+
+There are several ways to integrate this FastMCP server with Cursor IDE. Choose the method that works best for your setup.
+
+> **✅ Your Current Configuration is Already Optimal!** 
+> 
+> Looking at your `.cursor/mcp.json`, you're already using `uvx` which is the recommended approach. Your configuration with `uvx` + `mcp_server.py` is perfect for modern FastMCP development!
+
+### Method 1: Cursor Settings UI (Recommended)
+
+This is the easiest method for beginners:
+
+1. **Open Cursor Settings**:
+   - Go to `Settings → Cursor Settings → Features → Model Context Protocol`
+   - OR use `Cmd/Ctrl + ,` and search for "MCP"
+
+2. **Add New MCP Server**:
+   - Click **"Add New MCP Server"**
+   - Fill in the configuration:
+
+```
+Name: inference-providers
+Command: uvx
+Arguments: mcp_server.py
+Environment Variables:
+  HF_TOKEN: hf_your_actual_token_here
+```
+
+**Why UV/UVX?** ✨
+- **Faster**: UV is significantly faster than pip for dependency management
+- **Auto-manages dependencies**: Automatically handles virtual environments and packages
+- **Modern**: The recommended approach for Python tooling in 2025
+- **No setup required**: Works without manual virtual environment creation
+
+3. **Save and Test**:
+   - Click **"Add"** to save
+   - Restart Cursor
+   - Open a new chat and try: *"Use the chat completion tool to ask Groq about Python"*
+
+### Method 2: Project-Specific Configuration (Recommended)
+
+Create a `.cursor/mcp.json` file in your project root:
+
+```json
+{
+  "mcpServers": {
+    "inference-providers": {
+      "command": "uvx",
+      "args": ["mcp_server.py"],
+      "env": {
+        "HF_TOKEN": "hf_your_actual_token_here"
+      }
+    }
+  }
+}
+```
+
+**Advantages**: 
+- ✅ Project-specific (only available in this project)
+- ✅ Can be version controlled (but **don't commit tokens!**)
+- ✅ Automatic activation when opening the project
+- ✅ UV automatically handles dependencies from `pyproject.toml`
+
+### Method 3: Global Configuration
+
+Create a global configuration file:
+
+**Linux/macOS**: `~/.cursor/mcp.json`
+**Windows**: `%USERPROFILE%\.cursor\mcp.json`
+
+```json
+{
+  "mcpServers": {
+    "inference-providers": {
+      "command": "uvx",
+      "args": ["/full/path/to/your/project/mcp_server.py"],
+      "env": {
+        "HF_TOKEN": "hf_your_actual_token_here"
+      }
+    }
+  }
+}
+```
+
+**Advantages**:
+- ✅ Available across all Cursor projects
+- ✅ Set once, use everywhere
+
+### Method 4: Environment Variables (Most Secure)
+
+If you have `HF_TOKEN` set as a system environment variable, you can use:
+
+```json
+{
+  "mcpServers": {
+    "inference-providers": {
+      "command": "uvx",
+      "args": ["mcp_server.py"]
+    }
+  }
+}
+```
+
+The server will automatically pick up `HF_TOKEN` from your environment.
+
+## 🔄 UV vs Python: When to Use Which?
+
+| Approach | Best For | Pros | Cons |
+|----------|----------|------|------|
+| **`uvx` (Recommended)** | Most users, development | ⚡ Fast, auto-manages deps, modern | Requires UV installation |
+| **`python`** | System restrictions, debugging | 🔧 Universal, explicit control | Manual venv management |
+| **`uv run`** | Local development | 🎯 Project-aware, consistent | Must be in project directory |
+
+### UV Installation
+
+If you don't have UV installed:
+
+```bash
+# macOS/Linux
+curl -LsSf https://astral.sh/uv/install.sh | sh
+
+# Windows
+powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
+
+# Alternative: pip install
+pip install uv
+```
+
+## 🎮 Using the Server in Cursor
+
+Once configured, you can use the server in several ways:
+
+### 1. Let Cursor Auto-Select Tools
+
+Simply describe what you want:
+
+```
+"Help me compare language models for code generation"
+"Get recommendations for the best chat models"
+"I need to chat with a model about Python best practices"
+```
+
+Cursor will automatically detect and use the appropriate tools.
+
+### 2. Explicitly Request Tools
+
+Be more specific about which tool to use:
+
+```
+"Use the chat_completion tool with DeepSeek V3 via Novita to explain machine learning"
+"Call the inference providers chat tool to ask Groq about async programming"
+```
+
+### 3. Access Resources
+
+Get information about providers and models:
+
+```
+"Show me the available inference providers"
+"What are the popular models I can use?"
+"Get the provider capabilities information"
+```
+
+### 4. Generate Prompts
+
+Use the prompt generation feature:
+
+```
+"Generate a prompt to compare chat providers"
+"Create a comparison prompt for vision language models"
+```
+
+## 🎪 Example Conversations
+
+### Basic Chat Example
+
+```
+You: "Use the chat completion tool with Groq and Llama 3.1 70B to explain async/await in Python"
+
+Cursor: [Calls chat_completion tool]
+- Provider: groq
+- Model: meta-llama/Llama-3.1-70B-Instruct  
+- Message: "Explain async/await in Python with examples"
+
+Response: [Detailed explanation of async/await...]
+```
+
+### Provider Comparison Example
+
+```
+You: "Help me choose between Groq and Together AI for coding tasks"
+
+Cursor: [Uses provider comparison prompt and chat completion]
+Response: [Detailed comparison of providers with recommendations...]
+```
+
+### Model Recommendations
+
+```
+You: "What are good models for vision tasks?"
+
+Cursor: [Accesses models/popular resource]
+Response: Here are the recommended vision models:
+- meta-llama/Llama-3.2-11B-Vision-Instruct (Together)
+- microsoft/Phi-3.5-vision-instruct (HF Inference)
+- command-r-plus-vision (Cohere)
+```
+
+## 🔧 Advanced Configuration
+
+### Running as Remote Server
+
+For team usage or remote development:
+
+```bash
+# Option 1: Using UV (Recommended)
+uvx mcp_server.py --transport=sse --host=0.0.0.0 --port=8000
+
+# Option 2: Using Python directly
+python -c "
+from mcp_server import mcp
+mcp.run(transport='sse', host='0.0.0.0', port=8000)
+"
+```
+
+Then configure Cursor to connect remotely:
+
+```json
+{
+  "mcpServers": {
+    "inference-providers": {
+      "command": "npx",
+      "args": [
+        "-y",
+        "@modelcontextprotocol/client-remote",
+        "http://your-server:8000/sse"
+      ],
+      "env": {
+        "HF_TOKEN": "hf_your_token_here"
+      }
+    }
+  }
+}
+```
+
+### Alternative UV Commands
+
+Different ways to run with UV:
+
+```json
+{
+  "mcpServers": {
+    "inference-providers-uvx": {
+      "command": "uvx",
+      "args": ["mcp_server.py"],
+      "env": {"HF_TOKEN": "hf_your_token_here"}
+    },
+    "inference-providers-uv-run": {
+      "command": "uv", 
+      "args": ["run", "mcp_server.py"],
+      "env": {"HF_TOKEN": "hf_your_token_here"}
+    },
+    "inference-providers-uv-tool": {
+      "command": "uv",
+      "args": ["tool", "run", "mcp_server.py"],
+      "env": {"HF_TOKEN": "hf_your_token_here"}
+    }
+  }
+}
+```
+
+**Differences:**
+- **`uvx`**: Installs and runs in isolated environment (recommended)
+- **`uv run`**: Runs using project's pyproject.toml (project-aware)
+- **`uv tool run`**: Explicit tool execution (most explicit)
+
+## 🚨 Troubleshooting
+
+### Server Not Appearing in Cursor
+
+1. **Check Configuration Syntax**:
+   ```bash
+   # Validate JSON syntax
+   python -c "import json; print(json.load(open('.cursor/mcp.json')))"
+   ```
+
+2. **Verify Command Works**:
+   ```bash
+   # Test with UV (recommended)
+   uvx mcp_server.py
+   
+   # Or test with Python
+   python mcp_server.py
+   ```
+
+3. **Check UV Installation**:
+   ```bash
+   # Verify UV is installed
+   uv --version
+   uvx --version
+   ```
+
+4. **Check Token Format**:
+   - Token should start with `hf_`
+   - No quotes in environment variables
+   - Token has "Inference Providers" scope
+
+### Tool Not Working
+
+1. **Check Cursor Logs**:
+   - Go to `Help → Show Logs`
+   - Look for MCP-related errors
+
+2. **Test Server Manually**:
+   ```bash
+   # Test with UV
+   uvx test_mcp.py
+   
+   # Or with Python
+   python test_mcp.py
+   ```
+
+3. **Verify Dependencies**:
+   ```bash
+   # UV automatically handles dependencies, but you can check:
+   uv pip list
+   ```
+
+4. **Verify Token Permissions**:
+   - Go to [HF Settings](https://huggingface.co/settings/tokens)
+   - Ensure token has "Inference Providers" access
+
+### Common Error Messages
+
+| Error | Solution |
+|-------|----------|
+| `HF_TOKEN is required` | Set HF_TOKEN environment variable |
+| `Unknown provider: xyz` | Check provider name spelling |
+| `Import "fastmcp" could not be resolved` | Run `uv add fastmcp` or `pip install fastmcp` |
+| `Server failed to start` | Check UV/Python path and permissions |
+| `uvx: command not found` | Install UV: `curl -LsSf https://astral.sh/uv/install.sh \| sh` |
+| `Permission denied` | Check file permissions: `chmod +x mcp_server.py` |
+
+### Getting Help
+
+If you're still having issues:
+
+1. **Check our test script**: `python test_mcp.py`
+2. **Review Cursor MCP docs**: [https://docs.cursor.com/context/model-context-protocol](https://docs.cursor.com/context/model-context-protocol)
+3. **Check FastMCP docs**: [https://github.com/jlowin/fastmcp](https://github.com/jlowin/fastmcp)
+4. **Cursor Community**: [https://forum.cursor.com](https://forum.cursor.com)
+
+## 🤖 Available MCP Capabilities
+
+### 🛠️ Tools
+
+**`chat_completion`** - Generate chat completions using Hugging Face Inference Providers
+
+Parameters:
+- `provider`: Inference provider (cerebras, cohere, groq, novita, etc.)
+- `model`: Model ID from Hugging Face Hub
+- `messages`: Chat messages (JSON array or plain text)
+- `temperature`: Response randomness (0.0-2.0, default 0.7)
+- `max_tokens`: Maximum response length (1-4096, default 512)
+- `top_p`: Nucleus sampling (0.0-1.0, default 0.9)
+- `stream`: Stream response (boolean, default False)
+- `stop_sequences`: Stop sequences (comma-separated)
+- `frequency_penalty`: Frequency penalty (-2.0 to 2.0)
+- `presence_penalty`: Presence penalty (-2.0 to 2.0)
+- `hf_token`: Your Hugging Face token (optional, uses env var)
+
+### 📊 Resources
+
+**`providers`** - Get list of available inference providers and capabilities
+**`models/popular`** - Get curated recommendations for popular models
+
+### 💭 Prompts
+
+**`generate_provider_comparison_prompt`** - Generate prompts for comparing providers
+
+## 🚀 FastMCP Features Used
+
+- **@mcp.tool**: Exposes the chat completion function as an MCP tool
+- **@mcp.resource**: Provides access to provider and model information
+- **@mcp.prompt**: Generates helpful prompts for provider comparison
+- **Context**: Rich logging, error handling, and progress reporting
+- **Multiple Transports**: Supports stdio, SSE, and HTTP transports
+
+## 🎯 Popular Models to Try
+
+**Chat Models:**
+- `deepseek-ai/DeepSeek-V3-0324` (Novita)
+- `meta-llama/Llama-3.1-70B-Instruct` (Groq) 
+- `mistralai/Mixtral-8x7B-Instruct-v0.1` (Together)
+- `google/gemma-2-27b-it` (HF Inference)
+
+**Vision Language Models:**
+- `meta-llama/Llama-3.2-11B-Vision-Instruct` (Together)
+- `microsoft/Phi-3.5-vision-instruct` (HF Inference)
+
+## 📖 Technical Details
+
+This MCP server is built using:
+- **FastMCP v2+** - The fast, Pythonic way to build MCP servers
+- **Model Context Protocol (MCP)** - For standardized tool exposure
+- **Hugging Face Inference Providers** - For model access across providers
+- **Async/Await** - For efficient request handling
+- **Rich Context Logging** - For detailed operation tracking
+
+## 🔗 Links
+
+- [FastMCP GitHub](https://github.com/jlowin/fastmcp)
+- [FastMCP Documentation](https://gofastmcp.com)
+- [Cursor MCP Docs](https://docs.cursor.com/context/model-context-protocol)
+- [Model Context Protocol](https://modelcontextprotocol.io/)
+- [Inference Providers Documentation](https://huggingface.co/docs/inference-providers)
+- [Get HF Token](https://huggingface.co/settings/tokens)
+- [Cursor Community Forum](https://forum.cursor.com)
+
+## 📝 License
+
+This project is open source and available under the MIT License.

mcp_server.py

@@ -0,0 +1,309 @@
+import os
+import json
+import requests
+from typing import Dict, Any, Optional
+from fastmcp import FastMCP, Context
+
+# Initialize FastMCP server
+mcp = FastMCP("Inference Providers MCP Server")
+
+# Inference Providers configuration
+PROVIDERS = {
+    "cerebras": {
+        "name": "Cerebras",
+        "tasks": ["chat-completion"],
+        "base_url": "https://router.huggingface.co/cerebras",
+    },
+    "cohere": {
+        "name": "Cohere",
+        "tasks": ["chat-completion", "chat-completion-vlm"],
+        "base_url": "https://router.huggingface.co/cohere",
+    },
+    "fal-ai": {
+        "name": "Fal AI",
+        "tasks": ["chat-completion", "chat-completion-vlm"],
+        "base_url": "https://router.huggingface.co/fal-ai",
+    },
+    "featherless-ai": {
+        "name": "Featherless AI",
+        "tasks": ["chat-completion", "chat-completion-vlm"],
+        "base_url": "https://router.huggingface.co/featherless-ai",
+    },
+    "fireworks-ai": {
+        "name": "Fireworks",
+        "tasks": ["chat-completion", "chat-completion-vlm"],
+        "base_url": "https://router.huggingface.co/fireworks-ai",
+    },
+    "groq": {
+        "name": "Groq",
+        "tasks": ["chat-completion"],
+        "base_url": "https://router.huggingface.co/groq",
+    },
+    "hf-inference": {
+        "name": "HF Inference",
+        "tasks": ["chat-completion", "chat-completion-vlm"],
+        "base_url": "https://router.huggingface.co/hf-inference",
+    },
+    "hyperbolic": {
+        "name": "Hyperbolic",
+        "tasks": ["chat-completion", "chat-completion-vlm"],
+        "base_url": "https://router.huggingface.co/hyperbolic",
+    },
+    "nebius": {
+        "name": "Nebius",
+        "tasks": ["chat-completion", "chat-completion-vlm"],
+        "base_url": "https://router.huggingface.co/nebius",
+    },
+    "novita": {
+        "name": "Novita",
+        "tasks": ["chat-completion", "chat-completion-vlm"],
+        "base_url": "https://router.huggingface.co/novita",
+    },
+    "nscale": {
+        "name": "Nscale",
+        "tasks": ["chat-completion", "chat-completion-vlm"],
+        "base_url": "https://router.huggingface.co/nscale",
+    },
+    "replicate": {
+        "name": "Replicate",
+        "tasks": ["chat-completion", "chat-completion-vlm"],
+        "base_url": "https://router.huggingface.co/replicate",
+    },
+    "sambanova": {
+        "name": "SambaNova",
+        "tasks": ["chat-completion", "chat-completion-vlm"],
+        "base_url": "https://router.huggingface.co/sambanova",
+    },
+    "together": {
+        "name": "Together",
+        "tasks": ["chat-completion", "chat-completion-vlm"],
+        "base_url": "https://router.huggingface.co/together",
+    },
+}
+
+
+async def make_request(
+    provider: str,
+    endpoint: str,
+    payload: Dict[str, Any],
+    hf_token: str,
+    ctx: Optional[Context] = None,
+) -> Dict[str, Any]:
+    """Make a request to the inference provider"""
+    if not hf_token:
+        error_msg = (
+            "HF_TOKEN is required. Please set it in the environment or provide it."
+        )
+        if ctx:
+            await ctx.error(error_msg)
+        return {"error": error_msg}
+
+    provider_config = PROVIDERS.get(provider)
+    if not provider_config:
+        error_msg = f"Unknown provider: {provider}"
+        if ctx:
+            await ctx.error(error_msg)
+        return {"error": error_msg}
+
+    url = f"{provider_config['base_url']}/{endpoint}"
+    headers = {
+        "Authorization": f"Bearer {hf_token}",
+        "Content-Type": "application/json",
+    }
+
+    if ctx:
+        await ctx.info(f"Making request to {provider} ({url})")
+
+    try:
+        response = requests.post(url, headers=headers, json=payload, timeout=60)
+        response.raise_for_status()
+
+        if ctx:
+            await ctx.info(f"Request successful to {provider}")
+
+        return response.json()
+    except requests.exceptions.RequestException as e:
+        error_msg = f"Request failed: {str(e)}"
+        if ctx:
+            await ctx.error(error_msg)
+        return {"error": error_msg}
+
+
+@mcp.tool()
+async def chat_completion(
+    provider: str,
+    model: str,
+    messages: str,
+    ctx: Context,
+    temperature: float = 0.7,
+    max_tokens: int = 512,
+    top_p: float = 0.9,
+    stream: bool = False,
+    stop_sequences: str = "",
+    frequency_penalty: float = 0.0,
+    presence_penalty: float = 0.0,
+    hf_token: Optional[str] = None,
+) -> str:
+    """Generate chat completions using Hugging Face Inference Providers.
+
+    This tool allows you to chat with various language models through
+    different inference providers including Cerebras, Cohere, Fireworks,
+    Groq, and others.
+
+    Args:
+        provider: The inference provider to use (cerebras, cohere, fal-ai,
+                 featherless-ai, fireworks-ai, groq, hf-inference,
+                 hyperbolic, nebius, novita, nscale, replicate, sambanova,
+                 together)
+        model: The model ID from Hugging Face Hub
+               (e.g., 'deepseek-ai/DeepSeek-V3-0324')
+        messages: Either a JSON array of messages in OpenAI format or
+                 plain text for simple queries
+        temperature: Controls response randomness (0.0-2.0, default 0.7)
+        max_tokens: Maximum tokens in response (1-4096, default 512)
+        top_p: Nucleus sampling parameter (0.0-1.0, default 0.9)
+        stream: Whether to stream the response (default False)
+        stop_sequences: Comma-separated stop sequences (optional)
+        frequency_penalty: Penalize frequent tokens (-2.0 to 2.0)
+        presence_penalty: Penalize present tokens (-2.0 to 2.0)
+        hf_token: Your Hugging Face token with Inference Providers access
+                 (falls back to HF_TOKEN environment variable)
+
+    Returns:
+        The generated text response from the language model
+    """
+    # Get HF token from parameter or environment
+    token = hf_token or os.getenv("HF_TOKEN")
+    if not token:
+        await ctx.error("HF_TOKEN not provided and not found in environment")
+        return "Error: HF_TOKEN is required but not provided"
+
+    await ctx.info(f"Starting chat completion with {provider} provider")
+    await ctx.info(f"Model: {model}")
+
+    try:
+        # Parse messages
+        if messages.strip().startswith("["):
+            parsed_messages = json.loads(messages)
+            await ctx.info(f"Parsed {len(parsed_messages)} messages from JSON")
+        else:
+            parsed_messages = [{"role": "user", "content": messages}]
+            await ctx.info("Created single user message")
+
+        payload = {
+            "model": model,
+            "messages": parsed_messages,
+            "temperature": temperature,
+            "max_tokens": max_tokens,
+            "top_p": top_p,
+            "stream": stream,
+        }
+
+        # Add optional parameters
+        if stop_sequences.strip():
+            payload["stop"] = [s.strip() for s in stop_sequences.split(",")]
+            await ctx.info(f"Added stop sequences: {payload['stop']}")
+
+        if frequency_penalty != 0:
+            payload["frequency_penalty"] = frequency_penalty
+
+        if presence_penalty != 0:
+            payload["presence_penalty"] = presence_penalty
+
+        result = await make_request(
+            provider, "v1/chat/completions", payload, token, ctx
+        )
+
+        if "error" in result:
+            await ctx.error(f"API Error: {result['error']}")
+            return f"Error: {result['error']}"
+
+        if "choices" in result and len(result["choices"]) > 0:
+            response_text = result["choices"][0]["message"]["content"]
+            await ctx.info(f"Generated response with {len(response_text)} characters")
+            return response_text
+        else:
+            await ctx.warning("Unexpected response format")
+            return json.dumps(result, indent=2)
+
+    except json.JSONDecodeError as e:
+        error_msg = f"Invalid JSON format for messages: {str(e)}"
+        await ctx.error(error_msg)
+        return f"Error: {error_msg}"
+    except Exception as e:
+        error_msg = f"Unexpected error: {str(e)}"
+        await ctx.error(error_msg)
+        return f"Error: {error_msg}"
+
+
+@mcp.resource("file://providers")
+async def get_providers() -> str:
+    """Get the list of available inference providers and their capabilities.
+
+    Returns JSON information about all supported providers including their
+    supported tasks and base URLs.
+    """
+    return json.dumps(PROVIDERS, indent=2)
+
+
+@mcp.resource("file://models/popular")
+async def get_popular_models() -> str:
+    """Get a list of popular models for each provider.
+
+    Returns curated recommendations for models to try with each provider.
+    """
+    popular_models = {
+        "chat_models": {
+            "cerebras": ["llama3.1-70b"],
+            "cohere": ["command-r-plus"],
+            "groq": ["meta-llama/Llama-3.1-70B-Instruct"],
+            "novita": ["deepseek-ai/DeepSeek-V3-0324"],
+            "together": ["mistralai/Mixtral-8x7B-Instruct-v0.1"],
+            "hf-inference": ["google/gemma-2-27b-it"],
+        },
+        "vision_models": {
+            "cohere": ["command-r-plus-vision"],
+            "together": ["meta-llama/Llama-3.2-11B-Vision-Instruct"],
+            "hf-inference": ["microsoft/Phi-3.5-vision-instruct"],
+        },
+    }
+    return json.dumps(popular_models, indent=2)
+
+
+@mcp.prompt()
+def generate_provider_comparison_prompt(task: str = "chat") -> str:
+    """Generate a prompt to help compare different inference providers.
+
+    Args:
+        task: The type of task to compare providers for (default: "chat")
+
+    Returns:
+        A prompt that can be used to get comparative analysis of providers
+    """
+    available_providers = [
+        name
+        for name, config in PROVIDERS.items()
+        if f"{task}-completion" in config["tasks"]
+    ]
+
+    providers_list = ", ".join(available_providers)
+
+    return f"""Please compare the following inference providers for {task} tasks:
+
+Providers: {providers_list}
+
+Consider factors like:
+- Model selection and capabilities
+- Performance and speed
+- Pricing (if known)
+- Special features or limitations
+- Use case recommendations
+
+Provide a balanced comparison that helps choose the right provider."""
+
+
+if __name__ == "__main__":
+    # Run the MCP server
+    # Default: stdio transport for local development
+    # For production, use: mcp.run(transport="sse", host="0.0.0.0", port=8000)
+    mcp.run()

pyproject.toml

@@ -0,0 +1,11 @@
+[project]
+name = "inference-providers-mcp"
+version = "0.1.0"
+description = "FastMCP Server for Hugging Face Inference Providers"
+readme = "README.md"
+requires-python = ">=3.11"
+dependencies = [
+    "fastmcp>=2.0.0",
+    "requests>=2.31.0",
+    "python-dotenv>=1.0.0"
+]

requirements.txt

@@ -0,0 +1,3 @@
+fastmcp>=2.0.0
+requests>=2.31.0
+python-dotenv>=1.0.0 

uv.lock

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