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We Built a 15-Agent System with Anthropic MCP: Here's When It Fails (and Shines)
4 raczekk 1 7/4/2025, 7:05:57 AM deepsense.ai ↗
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?