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As MCPs are adopted at scale, certain patterns start to emerge: 1. Increase in wrong tool calls 2. increase in token usage, semantic loss 3. agents skipping the required tool call sequence.
For high stakes domains such as finance and infrastructure, this leads to reliability and compliance risk.
This talk presents a new MCP server design pattern for addressing this class of problems called: "progressive tool exposure". Rather than assuming static tools, the MCP server actively controls which tools are visible to the agent at each point in execution. This framework ensures, tools are refreshed, scoped, and changed as the agent progresses through an MCP workflow. This allows the server to direct the agent’s action space, guide execution order, and enforce "runbooks" without changing server/ agent capability. This design pattern also tracks state ensuring backtracking of tool calls reverts the server side state, making agent behaviour as transactional on the MCP server.
We show how such practices reduce invalid tool calls, lower inference costs, and improve determinism for tool heavy systems.
Founder of Concierge AI. Ex-Uber building MCP systems at scale. Concierge AI manages 400+ public MCP deployments, Arnav focuses on MCP tool complexity and researches token overhead reduction at scale.
