Model Context Protocol

lineAug 12, 2025Original article

The Current State of LY Corporation (opens in new tab)

Tech-Verse 2025 showcased LY Corporation’s strategic shift toward an AI-integrated ecosystem following the merger of LINE and Yahoo Japan. The event focused on the practical hurdles of deploying generative AI, concluding that the transition from experimental models to production-ready services requires sophisticated evaluation frameworks and deep contextual integration into developer workflows. ## AI-Driven Engineering with Ark Developer LY Corporation’s internal "Ark Developer" solution demonstrates how AI can be embedded directly into the software development life cycle. * The system utilizes a Retrieval-Augmented Generation (RAG) based code assistant to handle tasks such as code completion, security reviews, and automated test generation. * Rather than treating codebases as simple text documents, the tool performs graph analysis on directory structures to maintain structural context during code synthesis. * Real-world application includes a seamless integration with GitHub for automated Pull Request (PR) creation, with internal users reporting higher satisfaction compared to off-the-shelf tools like GitHub Copilot. ## Quantifying Quality in Generative AI A significant portion of the technical discussion centered on moving away from subjective "vibes-based" assessments toward rigorous, multi-faceted evaluation of AI outputs. * To measure the quality of generated images, developers utilized traditional metrics like Fréchet Inception Distance (FID) and Inception Score (IS) alongside LAION’s Aesthetic Score. * Advanced evaluation techniques were introduced, including CLIP-IQA, Q-Align, and Visual Question Answering (VQA) based on video-language models to analyze image accuracy. * Technical challenges in image translation and inpainting were highlighted, specifically the difficulty of restoring layout and text structures naturally after optical character recognition (OCR) and translation. ## Global Technical Exchange and Implementation The conference served as a collaborative hub for engineers across Japan, Taiwan, and Korea to discuss the implementation of emerging standards like the Model Context Protocol (MCP). * Sessions emphasized the "how-to" of overcoming deployment hurdles rather than just following technical trends. * Poster sessions (Product Street) and interactive Q&A segments allowed developers to share localized insights on LLM agent performance and agentic workflows. * The recurring theme across diverse teams was that the "evaluation and verification" stage is now the primary driver of quality in generative AI services. For organizations looking to scale AI, the key recommendation is to move beyond simple implementation and invest in "evaluation-driven development." By building internal tools that leverage graph-based context and quantitative metrics like Aesthetic Scores and VQA, teams can ensure that generative outputs meet professional service standards.

Model Context Protocol AI Large Language Models RAG+5

figmaAug 5, 20253 min readCurated summary

Design Systems And AI: Why MCP Servers Are The Unlock | Figma Blog

Design systems provide the shared language AI agents need to generate code that is not merely functional, but consistent with a company’s brand, accessibility standards, and engineering practices. Figma argues that its MCP server unlocks this value by transferring design context—such as components, variables, styles, and code mappings—directly into developer workflows. The result is a feedback loop in which stronger design systems produce better AI output, while AI makes those systems more useful and scalable. ## Design Systems as Context for AI - Design systems already connect design and engineering through: - Shared patterns and terminology - Documentation and best practices - Brand guidelines and reusable code - These same elements give AI agents the context required to produce the “right” output rather than generic interfaces. - A mature design system can therefore become a productivity multiplier for AI-powered product development. - Organizations without a robust system can also use Figma’s MCP server to help implement tokens and components. ## Design Systems as the Shared Language - As AI lowers the barrier between ideas and implementation, product differentiation increasingly depends on craft, visual identity, and user experience. - Design systems help scale that craft while preserving speed, quality, and consistency. - Effective systems provide: - **Scalable foundations:** Tokens for color, spacing, typography, and other design decisions - **Reusable components:** Flexible elements built around a shared source of truth - **Built-in accessibility:** Inclusive experiences by default - They also prevent teams from shipping interchangeable, generic interfaces assembled from common AI-generated parts. ## Why Context Improves AI Code Generation - Figma reports that 68% of developers use AI to write code, but only 32% trust its output. - Without design-system context, AI behaves like a new engineer who has not been onboarded: its code may work, but it may not follow team conventions. - With that context, AI can: - Reuse existing components and patterns - Apply design tokens consistently - Generate higher-quality starting code - Reduce misunderstandings and shorten design-engineering feedback loops ## How Figma’s MCP Server Works - When developers inspect a Figma frame, the MCP server sends relevant context to an AI agent, including: - Components - Styles - Variables - Variable code syntax - **Code Connect** can map design elements to real code resources, allowing agents to use existing implementation libraries. - Even without these mappings, the server supplies styling information that helps agents create more design-informed code. - Automated design-system rule generation can scan a codebase and produce a structured rules file covering: - Token definitions - Component libraries - Style hierarchies - Naming conventions - This file gives AI agents system-level defaults, reducing the need for developers to repeat detailed instructions in every prompt. - Figma MCP also provides annotations that can communicate extra context, including accessibility and interaction behavior. ## The Design-System and AI Flywheel - Better design systems provide richer context to AI agents. - Better context leads to more accurate, on-brand code. - Improved AI output can make design-system adoption and maintenance more valuable. - This creates a reinforcing cycle: robust systems improve AI results, and AI helps teams apply and extend those systems more effectively. Teams seeking reliable AI-generated product code should treat their design system as essential infrastructure and connect it to development tools through mechanisms such as MCP, Code Connect, tokens, rules, and annotations.

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Model Context Protocol Figma Design Systems AI Agents+2

figmaJun 25, 20253 min readCurated summary

6 Skills Every Engineer Needs for the AI Era | Figma Blog

AI should not merely automate engineering work; it should expand how developers explore problems, collaborate, and create better products. Figma argues that engineers who thrive in the AI era will combine technical judgment with user empathy, experimentation, and the ability to direct AI effectively. The goal is to automate repetitive work while preserving—and strengthening—the meaningful parts of product development. ## Use AI for More Than Automation - AI should maximize engineers’ capabilities rather than simply reduce costs. - Developers still need to identify the right problems, understand users, and apply care and craft to their solutions. - Automating tedious tasks frees teams to focus on product meaning, collaboration, and user value. - Employers increasingly value engineers who understand why they are building something, not just how. ## Use Vibe Coding to Explore Possibilities - Vibe coding is presented as a way to explore the problem space, not just generate production code. - Conversational development lets teams test more possible solutions in parallel and quickly produce visual artifacts. - Tools such as Figma Make support rapid prototyping, iteration, and refinement across design and code. - AI-assisted exploration can improve user experience by helping teams consider user needs earlier rather than automating away that consideration. ## Harness Agentic Capabilities - The Model Context Protocol (MCP) enables AI tools such as Cursor and Copilot to communicate with other software. - Figma’s MCP server supplies design context to language models, improving design-informed code generation. - Better context can increase visual fidelity and help developers follow established component libraries and accessibility practices. - Agentic tools are most effective when they have access to the conventions and information that guide the product. ## Audit Your Own Pull Requests - Engineers can use LLMs as a pre-review sounding board before submitting a pull request. - Models familiar with the codebase can identify duplicated implementations, unnecessary rewrites, and other issues. - This self-review improves code quality while reducing the burden on human reviewers. - AI-assisted review can increase engineering throughput without replacing team review. ## Coordinate Teams of AI Agents - Developers are learning to divide complex problems into smaller tasks for multiple AI agents. - They must then evaluate and integrate the agents’ separate solutions. - A key emerging skill is writing detailed Markdown instructions and providing context, much like guiding an intern. - The supplied article ends mid-section, so the sixth skill and the remainder of this discussion are not included. Engineers should treat AI as a partner for exploration, feedback, and coordination—not simply as a code generator. Strong results depend on clear problem framing, relevant context, human judgment, and continued attention to users.

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Model Context Protocol AI Large Language Models AI Agents+2

lineJun 13, 2025Original article

AI and Writer's Partnership (opens in new tab)

LY Corporation is addressing the chronic shortage of high-quality technical documentation by treating the problem as an engineering challenge rather than a training issue. By utilizing Generative AI to automate the creation of API references, the Document Engineering team has transitioned from a "manual craftsmanship" approach to an "industrialized production" model. While the system significantly improves efficiency and maintains internal context better than generic tools, the team concludes that human verification remains essential due to the high stakes of API accuracy. ### Contextual Challenges with Generic AI Standard coding assistants like GitHub Copilot often fail to meet the specific documentation needs of a large organization. * Generic tools do not adhere to internal company style guides or maintain consistent terminology across projects. * Standard AI lacks awareness of internal technical contexts; for example, generic AI might mistake a company-specific identifier like "MID" for "Member ID," whereas the internal tool understands its specific function within the LY ecosystem. * Fragmented deployment processes across different teams make it difficult for developers to find a single source of truth for API documentation. ### Multi-Stage Prompt Engineering To ensure high-quality output without overwhelming the LLM's "memory," the team refined a complex set of instructions into a streamlined three-stage workflow. * **Language Recognition:** The system first identifies the programming language and specific framework being used. * **Contextual Analysis:** It analyzes the API's logic to generate relevant usage examples and supplemental technical information. * **Detail Generation:** Finally, it writes the core API descriptions, parameter definitions, and response value explanations based on the internal style guide. ### Transitioning to Model Context Protocol (MCP) While the prototype began as a VS Code extension, the team shifted to using the Model Context Protocol (MCP) to ensure the tool was accessible across various development environments. * Moving to MCP allows the tool to support multiple IDEs, including IntelliJ, which was a high-priority request from the developer community. * The MCP architecture decouples the user interface from the core logic, allowing the "host" (like the IDE) to handle UI interactions and parameter inputs. * This transition reduced the maintenance burden on the Document Engineering team by removing the need to build and update custom UI components for every IDE. ### Performance and the Accuracy Gap Evaluation of the AI-generated documentation showed strong results, though it highlighted the unique risks of documenting APIs compared to other forms of writing. * Approximately 88% of the AI-generated comments met the team's internal evaluation criteria. * The specialized generator outperformed GitHub Copilot in 78% of cases regarding style and contextual relevance. * The team noted that while a 99% accuracy rate is excellent for a blog post, a single error in a short API reference can render the entire document useless for a developer. To successfully implement AI-driven documentation, organizations should focus on building tools that understand internal business logic while maintaining a strict "human-in-the-loop" workflow. Developers should use these tools to generate the bulk of the content but must perform a final technical audit to ensure the precision that only a human author can currently guarantee.

Model Context Protocol AI Large Language Models Generative AI+4

figmaJun 10, 20252 min readCurated summary

The Long and Short of It: Issue no.11 | Figma Blog

AI is changing how people build, but Figma argues that it does not replace craft, quality, or emotional connection. The issue highlights tools and practices that combine automation with design intent, context, safety, and care. Its central conclusion is that meaningful work still requires human judgment and deliberate practice. ## Building with Figma Make - Figma Make is a prompt-to-code tool that turns natural-language prompts or static designs into interactive prototypes. - It can be used at different stages of the design and development process. - Figma recommends experimenting with prompts and applying practical techniques to get better results. ## Bringing Design Context into Coding - Agentic coding tools are limited when they lack context about the intended design. - Figma’s MCP server connects Figma files to AI coding tools. - It gives language models access to variables, components, and styles, helping them generate code that better reflects design intent. - This positions AI as part of the developer workflow rather than an isolated automation tool. ## When Efficiency Undermines Care - The issue examines the idea that excessive focus on efficiency can weaken connection and emotional investment in creative work. - Themes from Config 2025 include AI evolving from a tool into a teammate and the importance of reaching “minimum viable play.” - The broader message is that faster production should not come at the expense of thoughtful, resonant design. ## Designing AI with Trust and Transparency - Headspace’s Ebb AI mental-health companion was designed with trust and safety as priorities. - Product and brand teams considered details such as the character’s name, visual identity, and conversational guidelines. - The goal was to keep Ebb’s AI nature visible while ensuring users felt supported. - The example illustrates the additional responsibility involved in creating AI products for sensitive contexts. ## Craft Requires Practice - Figma’s third annual Config publication, *Practice*, explores how designers develop mastery. - It emphasizes patience, precision, experimentation, and a willingness to push boundaries. - The accompanying microsite, created with Other Means, includes a custom font by Kia Tasbihgou. ## Rabbit Hole - The issue closes with a visual collection of colorful abstract forms, collages, and photography, extending its focus on experimentation and creative inspiration. AI can accelerate making, but strong results still depend on context, care, taste, and practice. Figma’s recommendation is to use AI as a creative partner while preserving the human attention that gives work its meaning.

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Model Context Protocol Figma AI Large Language Models+3

figmaJun 4, 20252 min readCurated summary

Double Click: What Does MCP Mean for Agentic AI? | Figma Blog

MCP is emerging as a common interoperability layer between AI assistants and external tools or data sources. By allowing models to discover and invoke tools during a conversation, it can make agentic systems faster, more scalable, and less dependent on bespoke integrations. The growing ecosystem—including Figma’s own MCP server—suggests MCP could become foundational infrastructure for the agentic web. ## What MCP Is - Anthropic introduced the Model Context Protocol in November, 2024; support from OpenAI helped drive widespread adoption. - MCP standardizes how assistants such as Claude, Copilot, and Cursor communicate with tools and data. - Developers can avoid building separate custom integrations for every AI assistant and service. - The protocol is compared to: - **USB-C**, as a universal connection for AI applications. - **HTTP**, because it is lightweight, composable, interoperable, and largely unconcerned with the payload. - Microsoft CTO Kevin Scott described MCP as a potential backbone for agent communication and evolution. ## MCP and Agentic AI - MCP gives large language models real-time access to tools and information. - This enables AI agents to act on a user’s behalf rather than merely generate text or code. - The number of available MCP servers is growing rapidly. - Figma created an MCP server that connects design information directly to developer workflows, supporting design-informed code generation. ## MCP as an Accelerant - Traditional API-based workflows often require an LLM to generate code and execute it whenever it needs to interact with a tool. - With MCP, the model can recognize available tools and invoke them directly during a conversation. - This reduces friction and improves speed, efficiency, and scalability. - The resulting experience makes complex actions feel more immediate—users can increasingly “just do things.” ## Early Examples of MCP Use - An automated AI travel agency used four agents across Google Maps, Airbnb, Google Calendar, and Weather. - A Blender MCP server generated a 3D scene of a low-poly dragon guarding treasure from a few natural-language instructions. - Y Combinator hosted a large MCP hackathon, demonstrating the rapid growth of experimentation around the protocol. MCP’s main promise is not simply connecting AI to APIs, but creating a shared, extensible standard that lets agents use many tools dynamically. Its long-term impact will depend on how reliably and responsibly these increasingly capable systems operate.

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Model Context Protocol Figma Large Language Models AI Agents+1

figmaJun 3, 20253 min readCurated summary

Introducing our MCP server: Bringing Figma into your workflow | Figma Blog

Figma’s beta MCP server connects Figma to AI coding tools such as Cursor, Copilot in VS Code, Windsurf, and Claude Code. It gives LLMs richer design context than screenshots or API responses alone, helping them generate code that reflects a team’s design system, codebase patterns, and intended behavior. Figma argues that accurate design-to-code work requires a holistic understanding of both visual design and implementation context. ## Why Design Context Matters - LLMs can produce functional code without additional context, but it may not match a team’s: - Architecture and file structure - Framework and terminology - Existing components and workflows - Evolving codebase conventions - These team-specific patterns form a unique “fingerprint” that models cannot reliably infer from training data. - MCP provides a standardized way for applications such as Figma to supply targeted context to agentic AI tools. ## Translating Design Intent for LLMs - Human developers typically: - Zoom out to understand overall structure and layout - Examine screen sequences and application flows - Infer how designs should map to code files and components - Interpret placeholder content as real data or backend requirements - Move between high-level patterns and low-level implementation details - The Figma MCP server aims to give LLMs the same broad perspective. - Its tools expose different kinds of context, allowing users to control which information is included and avoid wasting context-window space. ## Pattern Metadata - Figma can provide references to specific: - Components - Variables and design tokens - Styles - Code files - This is especially useful when design and code are already aligned through a design system. - Code Connect can identify the exact code component associated with a Figma component, reducing unnecessary codebase searches and preventing duplicate implementations. - For design tokens, Figma can identify the precise variable used—even when several tokens share the same visual value. - If code syntax is defined for a variable, the MCP server can pass the exact implementation syntax to the LLM. - Supplying this metadata improves precision and reduces token usage. ## Screenshots - Screenshots supplement metadata when visual or interactive meaning is difficult to express structurally. - They can communicate: - Embedded or interactive content represented by imagery - Relationships between sections and nodes - Sequences of screens - Mobile and desktop layouts - Overall application flow - Screenshots are not intended as pixel-perfect specifications. - Figma emphasizes that generated code should reflect design intent rather than merely reproduce pixels. - Combining screenshots with Figma’s code-oriented outputs is more effective than relying on either alone. ## Interactivity and Behavior - Code examples and pseudocode can express behavior more effectively than raw design metadata. - They are useful for: - Stateful components - Encapsulated functionality - UI sequences and transitions - Differences between related states or screens - Pseudocode becomes more valuable when it incorporates codebase context, such as variable syntax and Code Connect component mappings. ## Beta Roadmap - The MCP server is an early beta release. - Figma plans to add remote server capabilities and deeper integrations with codebases. - The company is seeking feedback while continuing to expand the design-to-code workflow. Figma’s MCP server is most valuable when teams maintain strong alignment between their design systems and codebases. Combining structured metadata, visual context, and behavioral examples gives AI coding agents a better foundation for producing implementation-ready code.

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Model Context Protocol Figma Large Language Models AI Agents+3