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by jck411
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Python
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MCP Backend OpenRouter

A high-performance chatbot platform connecting MCP servers with LLM APIs for intelligent tool execution.

🚀 Quick Start

# Install dependencies uv install # Start the platform uv run python src/main.py # Reset configuration to defaults uv run mcp-reset-config

Connect: ws://localhost:8000/ws/chat

📡 WebSocket API

Send Messages

{ "type": "user_message", "message": "Hello, how can you help me today?" }

Receive Responses

{ "type": "assistant_message", "message": "I'm here to help! What would you like to know?", "thinking": "The user is greeting me...", "usage": { "prompt_tokens": 15, "completion_tokens": 12, "total_tokens": 27 } }

Message Types

Type

Purpose

Payload

user_message

Send user input

{"type": "user_message", "message": "text"}

clear_history

Start new session

{"type": "clear_history"}

assistant_message

AI response

{"type": "assistant_message", "message": "text", "thinking": "reasoning"}

tool_execution

Tool status

{"type": "tool_execution", "tool_name": "name", "status": "executing"}

⚙️ Configuration

Essential Settings (src/runtime_config.yaml)

chat: websocket: port: 8000 # WebSocket server port service: max_tool_hops: 8 # Maximum tool call iterations streaming: enabled: true # Enable streaming responses storage: persistence: db_path: "chat_history.db" retention: max_age_hours: 24 max_messages: 1000 llm: active: "openrouter" # Active LLM provider providers: openrouter: base_url: "https://openrouter.ai/api/v1" model: "openai/gpt-4o-mini" temperature: 0.7 max_tokens: 4096

MCP Servers (servers_config.json)

{ "mcpServers": { "demo": { "enabled": true, "command": "uv", "args": ["run", "python", "Servers/config_server.py"], "cwd": "/path/to/your/project" } } }

🔧 Performance Tuning

Streaming Optimization

chat: service: streaming: persistence: persist_deltas: false # Maximum speed (no DB writes during streaming) interval_ms: 200 # Flush every 200ms min_chars: 1024 # Or when buffer reaches 1024 chars

HTTP/2 Support

uv add h2 # Required for HTTP/2 optimization

🛠️ Development

Code Standards

  • Use uv for package management

  • Pydantic for data validation

  • Type hints required

  • Fail-fast error handling

Available Scripts

uv run python src/main.py # Start platform uv run python scripts/format.py # Format code uv run mcp-reset-config # Reset configuration

Code Formatting

# Quick format (ignores line length issues) ./format.sh # Full check including line length uv run ruff check src/ # Format specific files uv run ruff format src/chat/ src/clients/

📁 Project Structure

MCP_BACKEND_OPENROUTER/ ├── src/ # Main source code │ ├── main.py # Application entry point │ ├── config.py # Configuration management │ ├── websocket_server.py # WebSocket communication │ ├── chat/ # Chat system modules │ ├── clients/ # LLM and MCP clients │ └── history/ # Storage and persistence ├── Servers/ # MCP server implementations ├── config.yaml # Default configuration ├── runtime_config.yaml # Runtime overrides ├── servers_config.json # MCP server config └── uv.lock # Dependency lock file

🔑 Environment Variables

# Required for LLM APIs export OPENAI_API_KEY="your-key" export OPENROUTER_API_KEY="your-key" export GROQ_API_KEY="your-key"

🚨 Troubleshooting

Common Issues

Problem

Solution

Configuration not updating

Check file permissions on

runtime_config.yaml

WebSocket connection fails

Verify server is running and port is correct

MCP server errors

Check

servers_config.json

and server availability

LLM API issues

Verify API keys and model configuration

Debug Mode

# In runtime_config.yaml logging: level: "DEBUG"

Component Testing

# Test configuration from src.config import Configuration config = Configuration() print(config.get_config_dict()) # Test LLM client from src.clients.llm_client import LLMClient llm = LLMClient(config.get_llm_config())

✅ Features

  • Full MCP Protocol - Tools, prompts, resources

  • High Performance - SQLite with WAL mode, optimized indexes

  • Real-time Streaming - WebSocket with delta persistence

  • Multi-Provider - OpenRouter (100+ models), OpenAI, Groq

  • Type Safe - Pydantic validation throughout

  • Dynamic Configuration - Runtime changes without restart

  • Auto-Persistence - Automatic conversation storage

📚 Quick Reference

Command

Purpose

uv run python src/main.py

Start the platform

uv run mcp-reset-config

Reset to default config

Edit

runtime_config.yaml

Change settings (auto-reload)

Edit

servers_config.json

Configure MCP servers

🆘 Support

  • Check logs for detailed error messages

  • Verify configuration syntax with YAML validator

  • Test individual components for isolation

  • Monitor WebSocket connections and database size

Requirements: Python 3.13+, uv package manager

This server cannot be installed

-
security - not tested
F
license - not found
-
quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

High-performance Model Context Protocol server supporting multiple LLM providers (OpenRouter, OpenAI, Groq) with WebSocket API and conversation history persistence.

  1. 🚀 Quick Start
    1. 📡 WebSocket API
      1. Send Messages
      2. Receive Responses
      3. Message Types
    2. ⚙️ Configuration
      1. Essential Settings (src/runtime_config.yaml)
      2. MCP Servers (servers_config.json)
    3. 🔧 Performance Tuning
      1. Streaming Optimization
      2. HTTP/2 Support
    4. 🛠️ Development
      1. Code Standards
      2. Available Scripts
      3. Code Formatting
    5. 📁 Project Structure
      1. 🔑 Environment Variables
        1. 🚨 Troubleshooting
          1. Common Issues
          2. Debug Mode
          3. Component Testing
        2. ✅ Features
          1. 📚 Quick Reference
            1. 🆘 Support

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