🚀 MCP Router

Intelligent Model Context Protocol Router for Cursor IDE

Automatically selects the optimal LLM model for each task based on query analysis, complexity, and your preferred strategy.

📐 System Architecture

┌─────────────────────────────────────────────────────────────────────────────┐
│                              CURSOR IDE                                      │
│  ┌────────────────────────────────────────────────────────────────────────┐ │
│  │                         User Query                                      │ │
│  │   "Refactor this authentication system across multiple files"          │ │
│  └──────────────────────────────┬─────────────────────────────────────────┘ │
│                                 │                                            │
│                                 ▼                                            │
│  ┌────────────────────────────────────────────────────────────────────────┐ │
│  │                      MCP Router Server                                  │ │
│  │  ┌──────────────────┐    ┌───────────────────┐   ┌──────────────────┐  │ │
│  │  │  Query Analyzer  │───▶│   Model Scorer    │──▶│ Routing Decision │  │ │
│  │  │                  │    │                   │   │                  │  │ │
│  │  │ • Task Type      │    │ • Quality Score   │   │ • Selected Model │  │ │
│  │  │ • Complexity     │    │ • Cost Score      │   │ • Confidence     │  │ │
│  │  │ • Requirements   │    │ • Speed Score     │   │ • Reasoning      │  │ │
│  │  │ • Token Estimate │    │ • Strategy Weight │   │ • Alternatives   │  │ │
│  │  └──────────────────┘    └───────────────────┘   └──────────────────┘  │ │
│  └──────────────────────────────┬─────────────────────────────────────────┘ │
│                                 │                                            │
│                                 ▼                                            │
│  ┌────────────────────────────────────────────────────────────────────────┐ │
│  │                     Model Registry (17 Models)                          │ │
│  │                                                                         │ │
│  │  ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────────┐   │ │
│  │  │  FLAGSHIP   │ │  REASONING  │ │ NATIVE/FAST │ │  BUDGET/LEGACY  │   │ │
│  │  │             │ │             │ │             │ │                 │   │ │
│  │  │ • GPT-5.2   │ │ • o3        │ │ • Composer1 │ │ • GPT-4o-mini   │   │ │
│  │  │ • Claude4.5 │ │ • o3-mini   │ │ • Gemini 3  │ │ • Claude Haiku  │   │ │
│  │  │   Opus     │ │ • Claude3.7 │ │   Pro/Flash │ │ • DeepSeek V3   │   │ │
│  │  │ • Claude4.5 │ │   Sonnet   │ │             │ │ • DeepSeek R1   │   │ │
│  │  │   Sonnet   │ │             │ │             │ │                 │   │ │
│  │  └─────────────┘ └─────────────┘ └─────────────┘ └─────────────────┘   │ │
│  └──────────────────────────────┬─────────────────────────────────────────┘ │
│                                 │                                            │
│                                 ▼                                            │
│  ┌────────────────────────────────────────────────────────────────────────┐ │
│  │                    Cursor Executes Query                                │ │
│  │            (Using its own API keys for selected model)                  │ │
│  └────────────────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────────┘

Data Flow

┌──────────┐      ┌──────────────┐      ┌───────────────┐      ┌────────────┐
│  Query   │─────▶│   Analyze    │─────▶│     Score     │─────▶│  Recommend │
└──────────┘      └──────────────┘      └───────────────┘      └────────────┘
                         │                      │                      │
                         ▼                      ▼                      ▼
                  ┌─────────────┐       ┌─────────────┐       ┌─────────────┐
                  │ Task Type:  │       │ Apply       │       │ Model:      │
                  │ • reasoning │       │ Strategy:   │       │ Claude 4.5  │
                  │ • code_gen  │       │ • balanced  │       │ Sonnet      │
                  │ • edit      │       │ • quality   │       │             │
                  │ Complexity: │       │ • speed     │       │ Confidence: │
                  │ • medium    │       │ • cost      │       │ 88.45%      │
                  └─────────────┘       └─────────────┘       └─────────────┘

✨ Features

Feature	Description
🤖 Intelligent Routing	Automatically selects the best model based on query analysis
🧠 Context-Aware Routing	Uses chat history and conversation context for smarter model selection
📊 4 Routing Strategies	`balanced` / `cost` / `speed` / `quality`
🔍 Query Analysis	Detects task type, complexity, and special requirements
💬 Chat History Analysis	Analyzes conversation patterns, topics, files, languages, and complexity
💰 Cost Estimation	Estimates costs before execution
⚡ 17 Models	Latest 2025 models from OpenAI, Anthropic, Google, Cursor, DeepSeek
🔧 Cursor Native	Zero API keys needed - Cursor handles execution

🏆 Supported Models (2025)

Tier 1: Flagship Models (Complex Architecture & Refactoring)

Model	Provider	Context	Cost (in/out)	Quality
GPT-5.2	OpenAI	256K	$5.00/$15.00	0.99/0.98
Claude 4.5 Opus	Anthropic	200K	$25.00/$75.00	0.99/0.99
Claude 4.5 Sonnet	Anthropic	200K	$5.00/$25.00	0.97/0.98

Tier 2: Reasoning Models (Chain of Thought)

Model	Provider	Context	Cost (in/out)	Quality
o3	OpenAI	200K	$10.00/$40.00	0.99/0.95
o3-mini (High)	OpenAI	128K	$1.50/$6.00	0.95/0.92
Claude 3.7 Sonnet	Anthropic	200K	$4.00/$20.00	0.96/0.96

Tier 3: Native & Fast Models

Model	Provider	Context	Cost (in/out)	Quality
Composer 1	Cursor	128K	$0.10/$0.30	0.88/0.92
Gemini 3 Pro	Google	2M	$2.00/$8.00	0.96/0.94
Gemini 3 Flash	Google	1M	$0.10/$0.40	0.88/0.90

Tier 4: Budget/Legacy Models

Model	Provider	Context	Quality
GPT-4o / GPT-4o-mini	OpenAI	128K	0.95/0.85
Claude 3.5 Sonnet/Haiku	Anthropic	200K	0.96/0.88
Gemini 2.0 Pro/Flash	Google	2M/1M	0.94/0.85
DeepSeek V3	DeepSeek	128K	0.92/0.94
DeepSeek R1	DeepSeek	128K	0.96/0.92

🚀 Quick Start

1. Install

git clone https://github.com/AI-Castle-Labs/mcp-router.git
cd mcp-router
pip install -r requirements.txt
pip install mcp  # MCP SDK for Cursor integration

2. Configure Cursor

Add to ~/.cursor/mcp.json:

{
  "version": "1.0",
  "mcpServers": {
    "mcp-router": {
      "command": "python3",
      "args": ["/path/to/mcp-router/src/mcp_server.py"],
      "env": {}
    }
  }
}

Note: No API keys needed! Cursor handles all API calls with its own keys.

3. Restart Cursor

The MCP router will appear in your agent tools. Use it with:

@mcp-router get_model_recommendation "your task description"
@mcp-router analyze_query "your query"
@mcp-router list_models

💻 CLI Usage

# Route a query (shows which model would be selected)
python main.py route "Explain how neural networks work"

# Route with strategy
python main.py route "Refactor this codebase" --strategy quality

# List all registered models
python main.py list

# Show routing statistics
python main.py stats

Example Output

============================================================
Routing Decision
============================================================
Query: Refactor this complex authentication system...

Selected Model: Claude 4.5 Sonnet
Model ID: claude-4.5-sonnet
Provider: anthropic
Confidence: 88.45%

Reasoning: Model is optimized for code_edit tasks; Selected for highest quality

Alternatives:
  - Composer 1 (composer-1)
  - Claude 3.5 Haiku (claude-3-5-haiku-20241022)
  - GPT-4o-mini (gpt-4o-mini)

🎯 Routing Strategies

Strategy	Description	Best For
`balanced`	Optimizes for cost, speed, and quality equally	General use
`quality`	Prioritizes highest capability models	Complex tasks, refactoring
`speed`	Prioritizes fastest response time	Quick edits, simple tasks
`cost`	Prioritizes cheapest models	Budget-conscious usage

🐍 Python API

from src.router import MCPRouter

# Initialize router (loads 17 default models)
router = MCPRouter()

# Route a query
decision = router.route(
    "Analyze this codebase architecture",
    strategy="quality"
)

print(f"Selected: {decision.selected_model.name}")
print(f"Model ID: {decision.selected_model.model_id}")
print(f"Confidence: {decision.confidence:.1%}")
print(f"Reasoning: {decision.reasoning}")

# Get alternatives
for alt in decision.alternatives[:3]:
    print(f"  Alternative: {alt.name}")

📁 Project Structure

mcp-router/
├── src/
│   ├── router.py          # Core routing logic + 17 model definitions
│   ├── mcp_server.py       # MCP server for Cursor integration
│   ├── client.py           # API client for model execution
│   └── cursor_wrapper.py   # Cursor-specific utilities
├── config/
│   └── cursor_mcp_config.json  # Template for Cursor config
├── scripts/
│   └── setup_cursor.sh     # Automated setup script
├── docs/
│   ├── cursor_integration.md
│   ├── QUICKSTART_CURSOR.md
│   └── AGENT_SETTINGS.md
├── main.py                 # CLI entry point
├── requirements.txt
└── README.md

🔧 Adding Custom Models

from src.router import MCPRouter, ModelCapabilities, TaskType

router = MCPRouter()

router.register_model(ModelCapabilities(
    name="My Custom Model",
    provider="custom",
    model_id="custom-model-v1",
    supports_reasoning=True,
    supports_code=True,
    supports_streaming=True,
    max_tokens=8192,
    context_window=32000,
    cost_per_1k_tokens_input=1.0,
    cost_per_1k_tokens_output=2.0,
    avg_latency_ms=600,
    reasoning_quality=0.85,
    code_quality=0.90,
    speed_score=0.80,
    preferred_tasks=[TaskType.CODE_GENERATION],
    api_key_env_var="CUSTOM_API_KEY"
))

🎮 Cursor Commands

Create .cursor/commands/route.md:

---
description: "Get model recommendation from MCP router for the current task"
---

Use the MCP router to determine the best model for the task at hand.

1. Analyze the current context
2. Call `@mcp-router get_model_recommendation` with task description
3. Present the recommendation with confidence and alternatives
4. Suggest switching models if needed

📊 MCP Tools Available

Tool	Description
`route_query`	Route a query and get model recommendation (supports chat_history)
`get_model_recommendation`	Get recommendation without execution (supports chat_history)
`analyze_chat_summary`	Analyze chat history text to extract routing signals
`list_models`	List all 17 registered models
`get_routing_stats`	Get usage statistics
`analyze_query`	Analyze query characteristics

Context-Aware Routing with Chat History

The router can now analyze chat history to make smarter routing decisions:

// Example: Using chat history for context-aware routing
{
  "query": "Fix the authentication bug we discussed",
  "strategy": "quality",
  "chat_history": [
    {
      "role": "user",
      "content": "I'm working on auth.py and users can't log in",
      "timestamp": 1704067200
    },
    {
      "role": "assistant",
      "content": "Let me check the authentication flow...",
      "timestamp": 1704067205
    }
  ]
}

The router analyzes chat history to detect:

Context depth: Shallow/medium/deep based on token count
Dominant task type: Code generation, editing, debugging, etc.
Programming languages: Detects Python, JavaScript, Rust, etc.
Files mentioned: Tracks files being worked on
Error patterns: Identifies debugging sessions
Topics: Authentication, database, API, testing, etc.
Complexity: Based on files, languages, and conversation depth

These signals influence model selection:

Deep context → Models with larger context windows
Debugging sessions → High-reasoning models
Multi-file tasks → Code-focused models
Multiple languages → Polyglot-capable models

🤝 Contributing

Fork the repository
Create a feature branch
Make your changes
Submit a pull request

📄 License

MIT License - see LICENSE for details.

Built for the Cursor IDE ecosystem
AI Castle Labs