We recently built a production-grade multi-agent system for document analysis using Anthropic’s Model Context Protocol (MCP). The system orchestrates 15+ agents with dynamic tool usage and task delegation.

Here's the deep dive: https://deepsense.ai/blog/standardizing-ai-agent-integration...

Key insights:

1. When MCP works best:

- Multiple agents sharing tools/resources

- Dynamic tool orchestration needs

- Rapid prototypes that must scale to production

2. When MCP is overkill:

- Simple static API integrations

- Performance-critical apps needing sub-ms latency

- When direct SDK calls are clearer

3. Practical takeaways:

- Design APIs for LLMs, not humans (strict typing = fewer errors)

- Limit tool access per agent (reduced hallucinations + ~50% token savings)

We also uncovered real security pitfalls in production and saw how model-task matching (e.g. Haiku vs Sonnet) affects performance and cost.

What are your experiences?