35 posts tagged with "MCP"

Today, MCP Apps are live as the first official MCP extension. Tools can now return interactive UI components that render directly in the conversation: dashboards, forms, visualizations, multi-step workflows, and more. It is ready for production.

I’ve been really interested in making AI actually useful in email — not “generic smart replies,” but an inbox where the right messages reliably turn into drafts that sound like me, reflect reality, and include the context I’d pull manually if I had time.

The catch: the context I need rarely lives inside Gmail.

It lives in:

• HubSpot (who is this person, what’s the account history, are they VIP, what did we promise?)
• Stripe (are they a customer, what plan, what happened with billing?)
• Postgres (internal source of truth: flags, entitlements, state)
• This repo (docs, runbooks, decisions, roadmaps, patterns)

So I built an AI-active Gmail inbox: Gmail stays the event source, but the agent runs inside a GitHub repo with MCP tools that can pull real context and draft replies that are personalized, accurate, and safe.

I did this without relying on Google’s AI. Gmail is plumbing (labels + push notifications). The intelligence and guardrails are mine.

Three non-negotiable constraints guided the build:

• No polling: I want push-driven events, not a cron job hammering APIs.
• No auto-send: drafts only; I approve every message.
• No inbox-only context: the agent must be able to query the systems that actually matter.

What I wanted was a workflow where Claude Code runs inside a GitHub repo (so it can read everything I’ve written and shipped), connects to MCP tools (so it can pull live customer context), drafts a reply in Gmail, and then pings me for approval. No surprises. No polling. No hallucinated promises.

We actually looked at building this with our Fastmail MCP first. I love Fastmail, but their API didn't give us a clean event trigger we could hook into without polling constantly. I hate polling. It feels messy and wasteful.

Gmail, however, has a push notification system that talks to Google Cloud Pub/Sub. That’s the hook.

Here's the war story of how I built a real-time, event-driven pipeline that turns "new important email" into "draft reply ready," with real context + personalization — and the mistakes I had to fix along the way.

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.

Your AI assistant calls a tool through MCP. What actually happened? Did it work? How long did it take? What did it send, and what did it get back?

Before now, you'd dig through logs or just hope your AI would explain what happened. That's not enough.

We built complete tool execution tracking right into MCP Bundles. Every tool call gets logged with full context—timing, credentials used, results, everything. You can see exactly what your AI is doing, debug failures fast, and understand your tool usage patterns.

Harvest has been the go-to time tracking tool for freelancers, agencies, and development teams for years. Whether you're billing clients hourly, managing project budgets, or just trying to understand where your time actually goes, Harvest makes it all work.

But here's the problem: your AI assistants have been locked out of this valuable data. Sure, you could export reports and paste them into ChatGPT, but that's not real integration. That's not having your AI actually understand and work with your time data.

That's why we built the Harvest MCP bundle.

Most teams now have an AI that can call tools, but someone has to keep those tools healthy. That someone is the MCP Engineer. They own how your assistants connect, which credentials stay active, and which bundles are safe to run on any given day.

Here's the exact playbook for using our Reddit bundle to find winning ad teardowns, copy their structure, and write your own "here’s what actually worked" breakdowns.

Most people stare at a blank page wondering what to write. We're going to skip that part entirely. Instead, we'll use the Reddit bundle to find what's already working, understand why it works, and then just plug in our own data.

You can run all of this right now in Bundle Studio.

If you're using Cursor (and honestly, why wouldn't you be), you've probably heard about MCP. It's how you give Claude access to your databases, APIs, and tools right inside the editor.

Here's how to actually set it up.

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.