● Covered by 1 source · 1 reportMedium impact1 neutral
Webflow Shares Lessons on Designing APIs for AI Agents
Key points
- Webflow started building for MCP in early 2025.
- Developer APIs are not suitable for AI agents due to reliance on human context.
- Webflow redesigned APIs around intents, simplifying schemas for agents.
- The approach focuses on agent-readable schemas and actionable error guidance.
Initial Approach and Challenges
Webflow began developing for MCP in early 2025, initially by wrapping existing APIs as MCP tools. This approach quickly revealed that APIs designed for human developers are not effective for AI agents. Developer APIs assume users can interpret documentation, research solutions, manage state, and handle failures manually, which AI agents lack.
Directly exposing these developer APIs led to low-level, chatty tools that required multiple calls for simple tasks and struggled with complex workflows. This resulted in subtle failures, inefficient runtimes, and unreliable task completion for agents.
Iterative Redesign for Agent Compatibility
Over the following year, Webflow iterated on its API design, shifting from endpoint-based tools to intent-based ones. This involved simplifying schemas, enhancing tool-calling efficiency, and making responses easier for agents to interpret and reason about. The company also invested in underlying infrastructure to support more reliable agent workflows.
These design decisions align with emerging industry patterns for agent-ready APIs, including task-oriented tools, agent-readable schemas, and actionable error guidance. The focus moved beyond simply exposing APIs through MCP to rethinking how autonomous systems discover capabilities, coordinate execution, and operate reliably.
Why Developer APIs Fall Short for Agents
Developer APIs typically break down tasks into granular, low-level operations. For example, updating a homepage hero might involve a chain of actions like listing pages, finding the homepage, fetching content, inspecting the content tree, finding the hero section, and then updating its content. AI agents struggle with this level of granularity without human-like contextual understanding.
Early MCP implementations that directly exposed these existing developer APIs as tools often led to inefficient and unreliable agent performance because agents require APIs that are self-guiding and provide explicit context for planning and execution.
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Reporting from
Webflow detailed its experience in developing agent-ready APIs for its MCP server, highlighting that APIs designed for human developers are not effective for AI agents. The company redesigned its tools to be intent-based, simplifying schemas and improving tool-calling efficiency for autonomous systems. This shift addresses the need for APIs that guide agent planning and execution without relying on implicit human context.