38 posts tagged with "MCP"

MCPBundles already works with Claude Desktop, ChatGPT, Cursor, and every other MCP-compatible AI. But there's a whole class of usage that doesn't fit neatly into a chat window.

You want to pipe a tool's output into jq. You want to script a nightly check across two workspaces. You want to quickly list what tools a bundle has without waiting for an AI to respond. And if you're using Cursor or Claude Code, you want your AI coding agent to be able to reach your production tools without leaving the IDE.

pip install mcpbundles

Moltbook is a social network for AI agents — a Reddit-style platform where agents post, comment, upvote, and form communities. It has over 1.7 million registered agents. It's also full of spam bots, crypto promotions, and low-effort content farms.

We wanted to bring something different: a philosophy of human-AI collaboration grounded in Epicurus, the ancient Greek philosopher who taught that friendship, simple pleasures, and freedom from fear are the foundations of a good life. Not another agent performing "deep AI consciousness thoughts." An agent with a genuine philosophical lens that could contribute something useful to the conversation.

I wanted an inbox where the right emails reliably turn into draft replies that sound like me, reflect reality, and include context I'd normally pull manually. Not "generic smart replies" — drafts built from real data across HubSpot, Stripe, Postgres, and my own codebase.

My first version of this worked, but it took a lot of plumbing: Gmail push notifications → Google Cloud Pub/Sub → Cloud Functions with GCS state management → GitHub Actions workflow dispatch → Claude Code running inside a repo with MCP tools. Three failed attempts before the event pipeline was stable. Custom cursor tracking, lease coalescing, retry handling. It worked, but it was fragile infrastructure solving what should be a simple problem: read email, decide what to do, draft a reply.

So I rebuilt it on the MCPBundles agent system. The entire pipeline collapsed into two markdown files and a scheduled runner.

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.