intelligentcode-ai/steering-mcp

Orchestration MCP Documentation

Overview

This directory contains the architectural documentation and implementation approaches for an AI-powered Orchestration MCP (Model Context Protocol) server designed to integrate with Claude Code and enforce proper behavioral patterns in AI agents.

Core Documents

📋 Current Architecture

• - Consolidated viable implementation approaches with AI integration options
• - System architecture, components, and behavioral enforcement patterns
• - Detailed deployment models (Cloud Services & Pre-Running Services)

🚀 Implementation Approaches

• - Solutions working within MCP's single-container constraints
• - Various methods for AI integration (Host leverage, APIs, CLI tools)
• - Using Claude/Gemini CLI tools inside Docker
• - Switching between CLI tools and external APIs

📚 Conceptual Documentation

• - Journey from initial vision to practical solutions, key learnings

Key Concepts

The Problem

Claude Code agents exhibit problematic patterns:

• Working in main scope instead of subagents
• Missing PRB generation
• False completion claims
• Lack of specialist role involvement

The Solution

An orchestration MCP server that:

• Enforces behavioral patterns through structured rules
• Leverages AI for intelligent decision making
• Generates PRBs automatically
• Coordinates multi-agent work

Implementation Options

1. Host AI Leverage - Return enforcement rules that Claude must follow (no infrastructure)
2. Cloud Services (Production) - Managed infrastructure for SaaS deployments (Supabase, Pinecone, Upstash)
3. Docker Compose Stack (Local) - PROPER services (PostgreSQL, Redis, Qdrant) with lightweight MCP client

Quick Start

Option 1: Minimal (Host AI Leverage)

{
  "mcpServers": {
    "orchestration": {
      "command": "docker",
      "args": ["run", "-i", "orchestration-mcp:minimal"]
    }
  }
}

Option 2: Cloud Services (Production)

export OPENAI_API_KEY=sk-...
export SUPABASE_URL=https://...
docker run -i orchestration-mcp:cloud

Option 3: Docker Compose Stack (Local Development)

# Start PROPER infrastructure services
docker-compose up -d  # PostgreSQL, Redis, Qdrant

# Run lightweight MCP client that connects to services
docker run -i orchestration-mcp:client

Archived Documents

The archive/ directory contains earlier exploration documents that were superseded by more practical approaches:

• TypeScript implementations (replaced with Python)
• Complex microservices architectures (incompatible with MCP)
• Pattern matching without AI (insufficient for requirements)

Key Constraints

• Single Process: MCP starts one Docker container via stdin/stdout
• No docker-compose: Cannot use multi-container orchestration as MCP
• Self-contained: Must work in isolation or with external services
• AI Required: Pattern matching alone insufficient for intelligent orchestration

Development Status

• ✅ Conceptual architecture complete
• ✅ Deployment options documented
• ✅ AI integration approaches defined
• 🚧 Implementation code pending
• 🚧 Testing and validation pending

This orchestration MCP server enables intelligent coordination of Claude Code agents while working within MCP's technical constraints.