8 posts tagged with "AI"

Figma is where product teams live — design files, component libraries, design tokens, comments, activity logs. But AI agents can't see inside Figma unless you give them structured access to the right data at the right granularity.

MCP (Model Context Protocol) solves this by letting AI agents call Figma as a set of typed tools. Instead of pasting screenshots into ChatGPT or manually describing your component hierarchy, the AI reads your files, inspects your components, checks your variables, and posts comments — all programmatically.

There are two ways to connect Figma via MCP: Figma's official MCP server and MCPBundles' 47-tool REST API bundle. This guide covers both.

Browser automation is one of the most powerful capabilities you can give an AI agent. Navigate to any page, read its content, fill forms, click buttons, take screenshots, inspect network traffic, run JavaScript — all programmatically through natural language.

Playwright is the industry standard for browser automation: fast, reliable, cross-browser, built for modern web apps. There are two ways to connect it to AI agents via MCP: Microsoft's official Playwright MCP server and MCPBundles' browser bundles with local and cloud deployment options.

Tool overload is real.

It shows up as lag. Wrong tool picks. Weird, half-finished workflows. Or the model just dumps a wall of raw data at you and calls it a day.

We’ve always had a simple answer: keep bundles focused. 5–15 tools for one job.

That still works great.

But sometimes you do want a big bundle. A real “everything I use for this role” bundle.

Now you can do that without turning your AI into a confused mess.

Every bundle can run in Dynamic.

Tool overload is real. Give AI 50 tools and it gets confused—slow, wrong tool selections, data dumps instead of answers. We've always solved this with focused bundles: give AI 5-15 tools for a specific workflow, and it works great.

But what about when you need data from multiple services at once?

That's why we built the Hub. It uses programmatic tool calling—AI discovers tools on-demand and writes code to orchestrate them—so you can work across all your connected services without the overload problem.

This builds on recent research from Anthropic—their work on advanced tool use and code execution with MCP. We took these patterns and made them accessible to anyone with an MCPBundles account.

Ask your AI about pipeline data and get a real dashboard. Not text. Not a description. An actual interactive visualization you can click, filter, and explore.

OpenAI's Sora can generate videos from text prompts. Seriously impressive stuff. But the API's a bit clunky to work with directly.

That's why we built the Sora MCP bundle. 6 tools that let you create, remix, and manage AI-generated videos without touching code. Just tell the AI what you want and it handles the rest.

This video was generated using the Sora MCP bundle—we used our own tools to create it.

Anthropic's recent blog post about code execution with MCP got everyone excited about converting tool calls into code. But I think we're optimizing the wrong thing.

Weaviate is an open-source vector database that powers AI-native applications—RAG systems, semantic search, recommendation engines, and more. But how do you make a vector database accessible to AI agents? You can't just expose raw API endpoints and expect good results.

The answer: 6 focused tools organized around what developers actually do with vector databases. Not 20 tools covering every edge case. Not 3 tools that force you into awkward patterns. Just 6 tools that handle search, storage, browsing, and management—the core workflows every vector database application needs.