LLM Bridge MCP: Seamless LLM Integration & Effortless Scaling

LLM Bridge MCP: Seamlessly connect any app to GPT, DeepSeek, Claude & more. One server. Infinite LLM possibilities. Deploy, switch, scale effortlessly.

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About LLM Bridge MCP

What is LLM Bridge MCP: Seamless LLM Integration & Effortless Scaling?

LLM Bridge MCP is a middleware solution that unifies access to multiple large language models (LLMs) via the Message Control Protocol (MCP). It enables developers to seamlessly integrate models from providers like OpenAI, Anthropic, Google, and DeepSeek into applications while simplifying model switching and scaling. This tool acts as a single entry point for managing diverse LLM capabilities with minimal code changes.

Key Features of LLM Bridge MCP

Universal LLM Access: Connect to major providers through a standardized interface
Type-Safe Development: Built on Pydantic AI for robust input validation
Flexible Parameter Control: Customize temperature, token limits, and system prompts per request
Usage Analytics: Track API consumption and performance metrics across models
Modular Design: Easily extend support for new LLM providers

LLM Bridge MCP Features

How to Use LLM Bridge MCP

Deployment follows three core steps:

Install Dependencies: Clone the repo and install the uv runtime framework
Configure Credentials: Set API keys in a .env file for each supported provider
Integrate with Tools: Register the MCP server in platforms like Claude Desktop or Cursor

Use the run_llm() function to send prompts, specifying model parameters directly in your application logic.

Use Cases of LLM Bridge MCP

Practical applications include:

AB testing across different LLMs without refactoring code
Dynamic model selection based on user preferences or workload demands
Cost optimization through automatic fallback to cheaper models
Rapid prototyping with multiple LLMs in development environments

LLM Bridge MCP FAQ

FAQ from LLM Bridge MCP

Q: How do I resolve the "spawn uvx ENOENT" error?

Specify the full path to the uvx executable in your MCP server configuration. Use which uvx (macOS/Linux) or where.exe uvx (Windows) to locate the executable.

Q: Can I add custom LLM providers?

Yes - the modular design allows extending supported providers by implementing MCP-compliant interfaces.

Q: What metrics does usage tracking capture?

Tracks API calls, token usage, response times, and error rates per model/provider combination.

Content

LLM Bridge MCP

LLM Bridge MCP allows AI agents to interact with multiple large language models through a standardized interface. It leverages the Message Control Protocol (MCP) to provide seamless access to different LLM providers, making it easy to switch between models or use multiple models in the same application.

Features

Unified interface to multiple LLM providers:
- OpenAI (GPT models)
- Anthropic (Claude models)
- Google (Gemini models)
- DeepSeek
- ...
Built with Pydantic AI for type safety and validation
Supports customizable parameters like temperature and max tokens
Provides usage tracking and metrics

Tools

The server implements the following tool:

run_llm(
    prompt: str,
    model_name: KnownModelName = "openai:gpt-4o-mini",
    temperature: float = 0.7,
    max_tokens: int = 8192,
    system_prompt: str = "",
) -> LLMResponse

prompt: The text prompt to send to the LLM
model_name: Specific model to use (default: "openai:gpt-4o-mini")
temperature: Controls randomness (0.0 to 1.0)
max_tokens: Maximum number of tokens to generate
system_prompt: Optional system prompt to guide the model's behavior

Installation

Clone the repository:

git clone https://github.com/yourusername/llm-bridge-mcp.git
cd llm-bridge-mcp

Install uv (if not already installed):

# On macOS
brew install uv

# On Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# On Windows
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Configuration

Create a .env file in the root directory with your API keys:

OPENAI_API_KEY=your_openai_api_key
ANTHROPIC_API_KEY=your_anthropic_api_key
GOOGLE_API_KEY=your_google_api_key
DEEPSEEK_API_KEY=your_deepseek_api_key

Usage

Using with Claude Desktop or Cursor

Add a server entry to your Claude Desktop configuration file or .cursor/mcp.json:

"mcpServers": {
  "llm-bridge": {
    "command": "uvx",
    "args": [
      "llm-bridge-mcp"
    ],
    "env": {
      "OPENAI_API_KEY": "your_openai_api_key",
      "ANTHROPIC_API_KEY": "your_anthropic_api_key",
      "GOOGLE_API_KEY": "your_google_api_key",
      "DEEPSEEK_API_KEY": "your_deepseek_api_key"
    }
  }
}

Troubleshooting

Common Issues

1. "spawn uvx ENOENT" Error

This error occurs when the system cannot find the uvx executable in your PATH. To resolve this:

Solution: Use the full path to uvx

Find the full path to your uvx executable:

# On macOS/Linux
which uvx

# On Windows
where.exe uvx

Then update your MCP server configuration to use the full path:

"mcpServers": {
  "llm-bridge": {
    "command": "/full/path/to/uvx",  // Replace with your actual path
    "args": [
      "llm-bridge-mcp"
    ],
    "env": {
      // ... your environment variables
    }
  }
}

License

This project is licensed under the MIT License - see the LICENSE file for details.