MCP Host CLI
About
Local http server that proxies requests o LLMs and uses mcp-servers if needed
Details
- Author
- VyacheslavVanin
- Downloads
- 132
- Categories
- Developer Tools
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- FastAPI server for chat interactions with LLMs
- Configurable via environment variables and CLI arguments
- Supports OpenAI-compatible providers (e.g., OpenRouter)
- Tool approval workflow with request/response endpoints
- Streaming mode and retry on LLM errors
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
MCP Host CLICommand (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 via environment variables (e.g., LLM_API_KEY, LLM_MODEL) or CLI arguments (e.g., --model, --port). Start the server using uv run main.py with desired options. Send requests to endpoints like POST /user_request and POST /approve.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"mcp host cli": {
"mcp-http-host": {
"command": "uv",
"args": [
"run",
"main.py"
]
}
}
}
}McpServers
{
"mcp-http-host": {
"command": "uv",
"args": [
"run",
"main.py"
]
}
}LLM Chat Server
FastAPI server for chat interactions with LLMs (OpenAI).
Configuration
Configuration is managed via environment variables and CLI arguments.
Environment Variables
| Variable | Description | Default |
|----------|-------------|---------|
| LLM_API_KEY | API key for LLM provider (alternative to --api-key-file) | - |
| LLM_MODEL | Model name to use | qwen2.5-coder:latest |
| PORT | Port to run server on | 8000 |
| LLM_PROVIDER | LLM provider (openai) | openai |
| OPENAI_BASE_URL | Base URL for OpenAI-compatible API | https://openrouter.ai/api/v1 |
Example .env file:
LLM_API_KEY=your-api-key
LLM_MODEL=qwen2.5-coder:latest
PORT=8000
LLM_PROVIDER=openai
CLI Arguments
| Argument | Description | Example |
|----------|-------------|---------|
| --model | LLM model to use | --model qwen2.5-coder:latest |
| --port | Port to run server on | --port 8000 |
| --provider | LLM provider (openai) | --provider openai |
| --openai-base-url | Base URL for OpenAI API | --openai-base-url https://api.openai.com/v1 |
| --api-key-file | Path to file containing the API key | --api-key-file /path/to/api.key |
| --servers-config | Path to servers config file | --servers-config config/servers.json |
| --current-directory | Working directory | --current-directory /projects |
| --context-window-size | Context window size | --context-window-size 2048 |
| --temperature | Temperature parameter | --temperature 0.7 |
| --stream | Enable streaming mode | --stream |
| --retries-on-llm-error | Number of retries on LLM error | --retries-on-llm-error 3 |
| --debug-messages | Enable debug messages logging to /tmp/llm-requester.messages.log | --debug-messages |
Example CLI usage:
# Using environment variable
export LLM_API_KEY=your-api-key
uv run main.py --model qwen2.5-coder:latest --port 8000
Using API key file
uv run main.py --model qwen2.5-coder:latest --port 8000 --api-key-file /path/to/api.key
API Endpoints
- POST /user_request - Handle user chat requests
- POST /approve - Handle tool approval/denial
- GET /session_state - Get current session state
- POST /start_session - Create new session
Request/Response Format
Request (POST /user_request)
{
"input": "user message"
}
Response
{
"request_id": "uuid",
"requires_approval": true,
"tool": {
"name": "tool-name",
"arguments": {
"arg1": "value1",
"arg2": "value2"
}
},
"message": "response content",
"model": "model-name",
"role": "assistant"
}
Tool Approval (POST /approve)
{
"request_id": "uuid",
"approve": true
}
Example Usage
```python
import requests
Start session
response = requests.post("http://localhost:8000/start_session", json={ "current_directory": "/projects", "model": "qwen2.5-coder:latest" })Send message
response = requests.post("http://localhost:8000/user_request", json={ "input": "Hello, how are you?" })Approve tool call
response = requests.post("http://localhost:8000/approve", json={ "request_id": "12345", "approve": true })Sign in to leave a review
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