Been deep in MCP (Model Context Protocol) server architecture this week, and the implications for automation builders are massive. The shift: Instead of rigid REST API integrations, MCP servers expose tools and resources that LLMs can discover and invoke dynamically. Think of it as USB-C for AI capabilities—standardized, hot-swappable, and context-aware. Technical implementation details: • **Transport layer**: stdio for local servers (Claude Desktop), HTTP SSE for remote. stdio has lower latency (~50ms vs 200ms+) but requires local execution • **Discovery**: The

tools/list

endpoint returns JSON schemas that the LLM uses to generate valid calls. This means zero-shot tool usage without hardcoded prompts • **Context injection**: Unlike webhooks, MCP maintains session state. When building a CRM automation, the server can expose previous customer interactions as resources, not just fire-and-forget events Real pattern I'm using: Building a lead enrichment pipeline where the MCP server exposes

search_company

and

verify_email

tools. The AI agent decides which to call based on data quality scores, rather than following a linear n8n flow. Error handling happens at the protocol level with proper JSON-RPC error codes. The tricky part is context window management—when your MCP server returns 10k tokens of company data, you need intelligent summarization before injection or you'll blow through Claude's 200k limit fast. Anyone else experimenting with custom MCP servers? Curious about your transport layer choices—stdio feels cleaner for local dev but harder to scale.