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
- License
- MIT
Explore
- 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
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
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
To install llm-bridge-mcp for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @sjquant/llm-bridge-mcp --client claude
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
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
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
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 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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