AI agents are breaking under tool overload.
Klavis AI thinks it has a fix.
This week, the YC-backed startup unveiled Strata, a new MCP server that helps agents manage thousands of tools through tiered reasoning. The company claims a 13% boost in benchmark success rates — a sharp improvement for developers struggling with complex toolchains.
Key Takeaways
- Klavis AI launches Strata MCP server to solve agent tool overload.
- New tiered reasoning design improves benchmark success rates by 13%.
- Human evaluation shows Strata hitting over 83% accuracy.
- Solution targets a pain point noted in Google Gemini tool studies.
Klavis AI’s Strata MCP server tackles the growing issue of AI agent tool overload by introducing tiered reasoning. This approach boosted benchmark success rates by 13% and achieved over 83% accuracy in human evaluations, offering developers a streamlined way to manage thousands of tools without system collapse.
Strata: A New Take on Tool Management
AI agents often stumble when given too many tools at once — a flaw documented in research from Google’s Gemini team. Klavis AI, a Y Combinator–backed startup, believes it has cracked the problem with Strata, a new MCP (Model Context Protocol) server that organizes tool usage into progressive tiers rather than overwhelming agents from the start.
Inside the Numbers
In benchmark testing, Strata lifted success rates by 13% compared to conventional tool access setups, according to Klavis AI. Human evaluations put Strata’s accuracy at 83%+, suggesting not just statistical gains but also more reliable real-world performance. The results were shared alongside an official GitHub release and a detailed demo.
AI agents fail when given too many tools – a lesson from our work on tool use at Google Gemini.
— Klavis AI (YC X25) (@Klavis_AI) September 22, 2025
So we're launching Strata: one MCP server for AI agents to handle thousands of tools progressively.
The Result? A +13% success rate boost on benchmarks & 83%+ accuracy on human eval.… pic.twitter.com/l8GntRGfDp
Industry Response
Developers and researchers have long flagged “tool overload” as one of the most common bottlenecks for agent deployment. Google Gemini research highlighted how adding too many tools often reduces performance rather than enhancing it. Klavis is pitching Strata as a fix for that paradox — one server that allows agents to scale tool use without collapsing under complexity.
Why It Matters
For AI startups and enterprise teams, managing tools is more than a technical challenge — it’s a cost and reliability issue. Failed tool calls can slow development cycles and increase operational overhead. Strata’s tiered reasoning could streamline workflows, enabling more resilient deployments across finance, healthcare, and productivity apps.
What Happens Next
Strata is now live on Klavis.ai and open source on GitHub. Klavis AI has not yet disclosed enterprise adoption figures, but early momentum suggests it could become a default infrastructure layer for MCP-based agents.
Conclusion
Tool overload has been a stubborn AI problem. Klavis AI’s Strata shows early signs of solving it — and if adoption scales, it may redefine how agents interact with sprawling tool ecosystems.