Bonnard's MCP Framework Designs AI Agent Tools for Accuracy and Reliability

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Bonnard has developed a structured approach to building Model Context Protocol (MCP) tools that AI agents can use effectively, addressing common failure points in agent-tool interactions. The framework uses a discovery-first design, where agents call a read-me tool to learn chart options and an explore-schema tool to understand database structure before executing any queries. Rather than offering one tool per metric or a single open-ended tool, Bonnard provides a small, purpose-built set — discover, query, and visualize — each with a narrow, well-defined function to prevent context overload. Responses are capped and flagged as partial or complete, errors include actionable fix hints, and deterministic tasks like axis inference and formatting are handled in code rather than left to the model. The result is a governed, consistent pipeline where agents self-correct and produce reliable visualizations without requiring manual prompt engineering.

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