AI

APIs in the age of AI

APIs in the age of AI: are they still just as important?

Artificial Intelligence (AI) is transforming software development, accelerating processes and providing tools that “understand” the context of our applications. However, a key question arises for any digital business: are APIs still just as important when AI can automate so many tasks? At APIQuality, we focus on optimizing the management and quality of your APIs, ensuring […]

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ai readiness scoring for apis

What is the AI Readiness scoring?

When evaluating an API’s readiness for LLM integration via the Model Context Protocol (MCP), we analyze four critical benchmarks: Developer Experience System Reliability Security & Safety Semantic Discovery Experience The API suffers from poor actionability due to descriptions referencing external APIs (‘use api-config to get all greetings templates’). Documentation quality is inconsistent, with examples sometimes

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API platform for LLMs

From Design-first to AI-first: Preparing Your API Platform for LLM-Driven Apps

In recent years, Large Language Models (LLMs) such as GPT-4, Gemini, and Claude have revolutionized software development. We no longer build applications only for human users — today, APIs are also consumed by AI agents that generate code, make decisions, and automate complete workflows. But here’s the challenge: traditional APIs, designed under the design-first (or

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CI/CD for APIs

From OpenAPI to production: how to set up API CI/CD with AI-generated test

In modern API development, releasing stable and reliable versions is not just a matter of code: it’s about building a specialised CI/CD pipeline, with automated testing, contractual validations, controlled deployments, and continuous monitoring.In this article, you will learn how to take your API from OpenAPI to production, leveraging language models (LLMs) to generate tests, integrating

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mcp apiquality for apis

How to use APIQuality with MCP to check the quality and security for your APIs

MCP Apiquality Now, you can use APIquality using MCP Servers. In this blog, we will to explain how to configure it: Pre-requirements Prerequisites: An active account on apiquality. A valid API token generated from the MCP login page: https://mcp.apiquality.io/token An MCP client, such as Visual Studio Code. An LLM subscription with agent capabilities (e.g., GitHub

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