LLM Bridge MCP
About
A model-agnostic Message Control Protocol (MCP) server that enables seamless integration with various Large Language Models (LLMs) like GPT, DeepSeek, Claude, and more.
Details
- Author
- sjquant
- GitHub stars
- 6
- Downloads
- 403
- Categories
- Cloud Service, AI, API
Jump to
- Unified interface to OpenAI, Anthropic, Google, and DeepSeek models.
- Built with Pydantic AI for type safety and validation.
- Supports customizable temperature and max tokens parameters.
- Provides usage tracking and metrics.
- Simple single-tool API: run_llm.
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
LLM Bridge MCPCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Configure API keys in a .env file (or pass them as environment variables) and then add a server entry to your Claude Desktop or Cursor configuration. The server exposes a single tool, run_llm, which accepts a prompt, optional model name (default openai:gpt-4o-mini), temperature, max tokens, and system prompt.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"llm bridge mcp": {
"llm-bridge-mcp": {
"command": "npx",
"args": [
"-y",
"@smithery/cli",
"install",
"@sjquant/llm-bridge-mcp",
"--client",
"claude"
]
}
}
}
}McpServers
{
"llm-bridge-mcp": {
"command": "npx",
"args": [
"-y",
"@smithery/cli",
"install",
"@sjquant/llm-bridge-mcp",
"--client",
"claude"
]
}
}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.
<a href="https://glama.ai/mcp/servers/@sjquant/llm-bridge-mcp">
</a>
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
Installing via Smithery
To install llm-bridge-mcp for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @sjquant/llm-bridge-mcp --client claude
Manual Installation
1. Clone the repository:
git clone https://github.com/yourusername/llm-bridge-mcp.git
cd llm-bridge-mcp
2. Install uv (if not already installed):
```bash
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