MCP enables powerful agent patterns where AI orchestrates multiple tools to accomplish complex tasks autonomously.

The Tool-Use Loop

The fundamental agent pattern:

1. User provides a goal
2. AI plans which tools to use
3. AI calls tools one at a time or in parallel
4. AI interprets results
5. AI decides if more tool calls are needed
6. AI provides the final answer

Common Agent Patterns

Research Agent:

• Search the web → Read relevant pages → Summarize findings → Cross-reference sources
• MCP servers: Brave Search + Puppeteer + File System

Code Agent:

• Read codebase → Identify issue → Write fix → Run tests → Commit
• MCP servers: File System + GitHub + Terminal

Data Agent:

• Query database → Analyze results → Generate charts → Write report
• MCP servers: PostgreSQL + File System

Customer Support Agent:

• Look up customer → Check order status → Search knowledge base → Draft response
• MCP servers: CRM + Order System + Knowledge Base + Email

Composing Multiple Servers

The power of MCP is composability. A single AI session can use tools from many servers:

{
  "mcpServers": {
    "github": { "command": "npx", "args": ["-y", "@mcp/server-github"] },
    "database": { "command": "npx", "args": ["-y", "@mcp/server-postgres", "..."] },
    "slack": { "command": "npx", "args": ["-y", "@mcp/server-slack"] },
    "filesystem": { "command": "npx", "args": ["-y", "@mcp/server-filesystem", "."] }
  }
}

Now the AI can: read code from GitHub, query the database, post updates to Slack, and write files — all in a single conversation.

Error Handling & Reliability

• Implement retries with exponential backoff in tool handlers
• Return clear error messages that help the AI recover
• Set timeouts to prevent hanging tool calls
• Log all tool invocations for debugging and auditing
• Validate inputs before executing expensive operations