Designing MCP servers for wide schemas and large result sets

Axiom has developed a compact context-driven MCP server to efficiently handle massive data volumes while minimizing token usage and latency, addressing challenges faced by AI assistants and clients dealing with verbose data payloads. By rethinking the data format, transitioning from JSON to CSV for tabular data, the server reduces token usage by approximately 29%, enhancing processing efficiency. Additionally, Axiom implements global cell budgets and heuristic scoring to prioritize high-value fields, ensuring relevant and concise data delivery. They also introduce techniques such as using maxBinAutoGroups to manage data granularity and maintain clarity without overwhelming the client with excessive context. The server's design emphasizes flexibility, allowing clients to tailor settings via the server URL to balance efficiency and data fidelity. Axiom's approach focuses on practical solutions, acknowledging trade-offs like reduced data resolution and encouraging client feedback to continually refine and adapt its MCP server for optimal performance in AI-driven environments.

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