AI & MCP Integration¶

Integrating AI agents, MCP servers, and multi-agent workflows with Claude Code

About This Series¶

This series documents production-ready patterns for integrating AI tools, Model Context Protocol (MCP) servers, and multi-agent orchestration into development workflows. Based on real implementations running in production.

Topics Covered¶

graph TD
    A[Claude Code] --> B[MCP Protocol]
    B --> C[Context7 - Docs]
    B --> D[Gemini MCP - AI-to-AI]
    B --> E[claude-flow - Swarms]
    B --> F[Playwright - Testing]

    A --> G[Profile System]
    G --> H[Token Optimization]
    G --> I[Workflow Modes]

    E --> J[Multi-Agent Tasks]
    D --> K[Code Review]
    D --> L[Second Opinions]

    style A fill:#e1f5ff
    style B fill:#fff4e1
    style E fill:#e8f5e8
    style D fill:#f3e5f5

Topic	Focus	Use Cases
MCP Integration	Tool connectivity	Library docs, browser testing, AI coordination
Token Management	Context efficiency	Profile-based loading, budget optimization
Multi-Agent	Swarm orchestration	Complex tasks, parallel execution
AI-to-AI	Model collaboration	Code review, second opinions, debates

Current Posts¶

1. Claude Code Profiles: Optimizing Token Usage ¶

Profile-based MCP loading architecture that optimizes token usage while maintaining full capabilities when needed.

Key Topics: - Token budget management (~2k to ~60k profiles) - Profile configurations (minimal, design, testing, orchestration, pro) - Unified vs split workflow architecture - Shell aliases for quick access

Tags: claude-code mcp ai-agents context-management prompt-engineering

2. Building a Gemini MCP Server ¶

Architecture and implementation of a production MCP server wrapping Google's Gemini CLI for AI-to-AI collaboration.

Key Topics: - Tool consolidation (44 → 5 tools, 93% token reduction) - Docker containerization with HTTP transport - OAuth authentication strategy - Streaming for long operations - AI debate capabilities

Tags: claude-code mcp gemini ai-agents docker automation

3. Multi-Agent AI Workflows with claude-flow ¶

Orchestrated multi-agent swarms within Claude Code for complex task decomposition and parallel execution.

Key Topics: - Swarm architecture and coordination - Profile-based swarm launcher - Self-healing wrapper scripts - When to use swarms vs direct execution - Resource management and efficiency

Tags: claude-code mcp ai-agents multi-agent swarm automation

Architecture Overview¶

MCP Server Ecosystem¶

┌─────────────────────────────────────────────────────────────────┐
│                     Claude Code                                  │
│                         │                                        │
│                         ▼                                        │
│              ┌─────────────────────┐                            │
│              │    MCP Protocol     │                            │
│              └─────────────────────┘                            │
│                         │                                        │
│    ┌────────────────────┼────────────────────┐                  │
│    │                    │                    │                  │
│    ▼                    ▼                    ▼                  │
│ ┌──────────┐     ┌──────────┐        ┌──────────┐             │
│ │ Context7 │     │ Gemini   │        │ claude-  │             │
│ │  (Docs)  │     │   MCP    │        │   flow   │             │
│ └──────────┘     └──────────┘        └──────────┘             │
│     │                 │                    │                    │
│     ▼                 ▼                    ▼                    │
│  Library          AI-to-AI            Multi-Agent              │
│   Docs            Collab               Swarms                   │
└─────────────────────────────────────────────────────────────────┘

Token Budget by Profile¶

Profile	Token Cost	MCP Servers	Use Case
`minimal`	~2k	context7	Quick fixes, debugging
`design`	~5k	+ magic	UI development
`testing`	~16k	+ playwright	E2E testing
`orchestration`	~57k	+ claude-flow	Multi-agent swarms
`pro`	~60k	Full stack	Complex workflows

Future Content¶

Planned additions to this series:

Qdrant Knowledge Base Integration - Semantic memory and RAG
Custom MCP Server Development - Building your own MCP tools
AI Code Review Patterns - Structured multi-model review workflows
Production MCP Deployment - Docker, Kubernetes, monitoring

Quick Start¶

Install Claude Code Profiles¶

# Create profile directory
mkdir -p ~/.config/claude-profiles

# Minimal profile (recommended starting point)
cat > ~/.config/claude-profiles/minimal.json << 'EOF'
{
  "mcpServers": {
    "context7": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "@upstash/context7-mcp@latest"]
    }
  }
}
EOF

# Add shell alias
echo 'alias claude-minimal="claude --mcp-config ~/.config/claude-profiles/minimal.json"' >> ~/.zshrc
source ~/.zshrc

Use a Profile¶

# Start Claude Code with minimal profile
claude-minimal

# Or with full capabilities
claude-pro

Last Updated: 2025-11-29 Series Status: Active (3 posts published) Total Posts: 3