Stop Loading 30 MCP Tools Into One Agent — 3 Design Patterns That Actually Work

I hit a wall building an AI agent with MCP. The agent had access to 30+ tools — database queries, file ops, Slack notifications, ticket management, you name it. And it kept making dumb decisions. Wrong tool calls. Hallucinated parameters. Context that made no sense. Then I read the research: at 32K tokens of context, 11 models tested dropped below 50% of their short-context baseline. Every tool schema you load burns tokens. More tools = more context rot = worse decisions. Here are the 3 design patterns I switched to — with working Python MCP server code for each one. Prerequisites Python 3.11+ mcp SDK: pip install mcp Basic familiarity with MCP servers and Claude agents 🔧 Pattern 1: Sub-Agent Grouping The idea: Don't wire 30 tools to one agent. Group tools by domain. Give each group its own focused MCP server. An orchestrator delegates; sub-agents specialize. 30 tools to 1 agent ❌ ↓ Orchestrator → DB Agent (5 tools) → Comms Agent (6 tools) → Storage Agent (4 tools) ✅ Each sub-agent onl