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API design for AI: Group endpoints by user workflow, not by API history

For large APIs, converting every endpoint into a single MCP server can overwhelm AI clients with too many similar or irrelevant tools. This makes it difficult for AI to select the correct function, leading to errors and inefficiency. The recommended solution is to design focused capability surfaces by grouping endpoints based on user workflows, such as customer support or billing, rather than simply exposing all available API functions. Creating separate, specialized MCP servers for distinct workflows enhances usability, simplifies permissions, and improves the AI's ability to accurately perform tasks. AI

IMPACT Optimizing API design for AI clients can improve agent performance and reduce integration complexity.

RANK_REASON Article discusses best practices for API design when integrating with AI clients, focusing on tool selection and organization.

Read on dev.to — MCP tag →

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API design for AI: Group endpoints by user workflow, not by API history

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22 / 100
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Article discusses best practices for API design when integrating with AI clients, focusing on tool selection and organization.
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product, infra
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High
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Breaking (< 6h)
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COVERAGE [1]

  1. dev.to — MCP tag TIER_1 English(EN) · Bhavy Shekhaliya ·

    What to Do When an API Has Too Many Endpoints for One MCP Server

    <p>Large APIs are where MCP design gets interesting.</p> <p>If your API has 15 endpoints, you can review each one by hand and decide which operations should become tools. If your API has 300 endpoints, exposing everything creates a different problem:</p> <blockquote> <p>the MCP s…