How Jenova Solved the AI Tool Scalability Problem That's Stalling MCP and Agentic AI

Agentic AI systems often lose reliability and efficiency as they are given larger tool inventories, because detailed tool definitions consume limited context space, increase latency and cost, and make it harder for language models to select appropriate tools and parameters. The text identifies this “tool overload” as a particular challenge for the Model Context Protocol ecosystem, where numerous discoverable third-party tools can create overlapping descriptions, interference, and “lost in the middle” attention effects. It reviews industry responses including server-side tool abstraction and hierarchical categories, as well as client-side routing that dynamically filters tools, while arguing that these measures alone retain limitations when relying on a single general-purpose model. It presents Jenova’s multi-agent mixture-of-experts approach as an alternative, using domain-specific routing, orchestration across models from different providers, and just-in-time loading of only relevant tool schemas. Jenova reports a 97.3% production tool-use success rate, though the broader conclusion is that scalable agent systems may require modular architectures that coordinate specialized agents and tightly scoped tool access rather than exposing every available tool to one model.

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