Metadata-Version: 2.3
Name: llms-bridge-mcp
Version: 0.1.2
Summary: MCP server to bridge LLM calls via Model Context Protocol
Author: Newton Winter
Author-email: Newton Winter <isoutthere@gmail.com>
Requires-Dist: fastmcp[tasks]>=3.0.0b1
Requires-Dist: pydantic>=2.0.0
Requires-Dist: typer>=0.16.0
Requires-Dist: httpx>=0.25.0
Requires-Dist: litellm>=1.81.4
Maintainer: Anton Kulaga, Newton Winter
Maintainer-email: Anton Kulaga <antonkulaga@gmail.com>, Newton Winter <isoutthere@gmail.com>
Requires-Python: >=3.11
Description-Content-Type: text/markdown

# llms-bridge-mcp

An MCP (Model Context Protocol) server to bridge LLM calls via the Model Context Protocol.

## Installation

Using `uv`:

```bash
uv sync
```

## Usage

### Run in stdio mode (for MCP clients like Claude Desktop)

```bash
uv run stdio
```

### Run in HTTP mode

```bash
uv run http --host 0.0.0.0 --port 3011
```

### Run in SSE mode

```bash
uv run sse --host 0.0.0.0 --port 3011
```

### Development mode with Smithery

```bash
uv run dev
```

## Available Tools

The server dynamically registers tools based on available API keys. Each tool allows you to call different LLM providers through the MCP interface.

### llms_bridge_call_chatgpt

Call OpenAI ChatGPT models with a prompt.

**Parameters:**
- `prompt` (str, required): The user message/prompt
- `model` (str, optional): OpenAI model (default: gpt-4o)
- `temperature` (float, optional): Sampling temperature 0-2 (default: 0.7)
- `max_tokens` (int, optional): Maximum response tokens (default: 4096)
- `system_message` (str, optional): System message for context

**Requires:** `OPENAI_API_KEY` environment variable

### llms_bridge_call_gemini

Call Google Gemini models with a prompt.

**Parameters:**
- `prompt` (str, required): The user message/prompt
- `model` (str, optional): Gemini model (default: gemini/gemini-2.0-flash-exp)
- `temperature` (float, optional): Sampling temperature 0-2 (default: 0.7)
- `max_tokens` (int, optional): Maximum response tokens (default: 4096)
- `system_message` (str, optional): System message for context

**Requires:** `GEMINI_API_KEY` environment variable

### llms_bridge_call_perplexity

Call Perplexity Sonar API for web-search enhanced responses.

**Parameters:**
- `prompt` (str, required): The user message/prompt
- `model` (str, optional): Perplexity model (default: perplexity/sonar)
- `temperature` (float, optional): Sampling temperature 0-2 (default: 0.7)
- `max_tokens` (int, optional): Maximum response tokens (default: 4096)
- `system_message` (str, optional): System message for context

**Requires:** `PERPLEXITY_API_KEY` environment variable

### llms_bridge_server_misconfiguration

This tool appears when no API keys are configured. It provides setup instructions.

## Testing

Run tests with pytest:

```bash
uv run pytest
```

## Configuration

### Environment Variables

Set the following environment variables:

- `OPENAI_API_KEY`: OpenAI API key for ChatGPT models
- `GEMINI_API_KEY`: Google Gemini API key
- `PERPLEXITY_API_KEY`: Perplexity API key
- `MCP_HOST`: Host to bind to (default: 0.0.0.0)
- `MCP_PORT`: Port to bind to (default: 3011)
- `MCP_TRANSPORT`: Transport type (default: streamable-http)

### MCP Configuration Files

The project includes several MCP configuration files for different deployment modes:

- `mcp-config-stdio.json` - Stdio mode for Claude Desktop and other MCP clients (uses uvx)
- `mcp-config-http.json` - HTTP mode for REST API integration
- `mcp-config-sse.json` - SSE (Server-Sent Events) mode for streaming
- `mcp-config-local.json` - Local development using uv

#### Using with Claude Desktop

For published package via uvx (recommended):
```bash
# Linux/Mac
cat mcp-config-stdio.json >> ~/Library/Application\ Support/Claude/claude_desktop_config.json

# Or manually merge the JSON content into your Claude config
```

For local development:
```bash
# Update the path in mcp-config-local.json to match your installation
# Then merge into Claude config
cat mcp-config-local.json >> ~/Library/Application\ Support/Claude/claude_desktop_config.json
```

#### Running via uvx (without installation)

```bash
# Stdio mode
uvx --prerelease=allow llms-bridge-mcp@latest stdio

# HTTP mode
uvx --prerelease=allow llms-bridge-mcp@latest http --host 0.0.0.0 --port 3011

# SSE mode
uvx --prerelease=allow llms-bridge-mcp@latest sse --host 0.0.0.0 --port 3011
```

## Features

- **Dynamic Tool Registration**: Tools are automatically registered based on available API keys
- **Multiple LLM Providers**: Support for OpenAI, Google Gemini, and Perplexity
- **Flexible Deployment**: Run via stdio, HTTP, or SSE transports
- **MCP Protocol**: Full Model Context Protocol support via FastMCP
- **LiteLLM Integration**: Unified interface to multiple LLM providers
- **Graceful Shutdown**: Proper signal handling for clean exits

## Project Structure

```
llms-bridge-mcp/
├── src/
│   └── llms_bridge_mcp/
│       ├── __init__.py
│       └── server.py           # Main server implementation
├── tests/
│   └── test_server.py          # Test suite including real LLM calls
├── mcp-config-stdio.json       # Stdio mode for Claude Desktop
├── mcp-config-http.json        # HTTP mode config
├── mcp-config-sse.json         # SSE mode config
├── mcp-config-local.json       # Local development config
├── .env.template               # Environment variables template
├── pyproject.toml              # Project dependencies and metadata
└── README.md
```
