AI agent development and design
View All TagsDirectories like Glama index over 20,000 MCP servers. Most of them are weekend projects. Some of them are brilliant. A surprising number of them just don't work.
We've been running MCPBundles for over a year — a platform where teams connect their AI agents to production APIs. We've tested, wrapped, and maintained MCP servers for hundreds of services. This guide is what we've learned about which ones are worth your time.
Claude Code is the best AI coding agent we've used. It runs in your terminal, has full access to your filesystem and shell, and writes real code against real projects. But out of the box, it can only work with what's on your machine — your files, your git repos, your local tools.
We kept hitting the same wall. We'd be deep in a debugging session and need to check a customer's Stripe payments, or pull someone's deal stage from HubSpot, or look at what queries are driving traffic in Google Search Console. Every time, we'd have to stop what we were doing, open a browser, log into a dashboard, click around, copy some data back, and paste it into the conversation. The AI had all the context about our codebase but zero visibility into the services our code actually talks to.
So we fixed it. We built a CLI that gives Claude Code authenticated access to 10,000+ tools across 500+ providers — every major SaaS platform, database, and API — and the AI handles everything. You just ask for what you need in plain English.
Here's the thing nobody tells you about Cursor's agent mode: it's brilliant at working with code and completely blind to everything your code talks to.
Last week we were debugging a webhook handler. Cursor had the code open, understood the control flow, spotted a race condition in the retry logic. Genuinely impressive. Then we needed to know whether the bug was actually hitting production — were customers seeing duplicate charges? The agent that just did 15 minutes of sophisticated code analysis couldn't answer a basic factual question about our own Stripe data.
So we opened a browser tab, logged into Stripe, searched for the customer, scrolled through PaymentIntents, compared timestamps manually, went back to Cursor, and typed what we found. The AI had all the context and none of the data.
We got tired of being the copy-paste bridge between our IDE and our dashboards.
Glama indexes 20,000+ MCP servers. Smithery has 8,000+. mcp.so has 6,000+. There's no shortage of servers to find.
The problem is everything that happens after you find one.
You pick a promising-looking Stripe MCP server from a directory. Now you need to clone the repo, install its dependencies (hope they don't conflict with yours), figure out whether it uses env or args for the API key, add your key to a JSON config file in plaintext, start the process, and configure your AI client to talk to localhost:3000. If you're lucky, it works. If the repo hasn't been updated in three months, it probably doesn't.
Repeat that for every service you want to connect. We got to five local MCP server processes before we gave up and built something better.
If you've set up an MCP server before, you know the drill. Clone a repo. Install dependencies. Add your API key to a JSON config file. Start the process. Configure your AI client to connect to localhost:3000. Repeat for every service you want to use.
It works. Until it doesn't. The process crashes silently. Your laptop sleeps and the server dies. You upgrade Node and the dependencies break. A teammate wants access and you're sharing API keys over Slack. You add a third service and now you're managing three server processes, three config files, and three sets of credentials in plaintext on your machine.
Local MCP servers are fine for trying things out. For daily use across a team, you need hosting.
The thing that makes Windsurf different from other AI editors is that Cascade is already watching. Open a file, and it's reading it. Switch to a terminal, and it sees the output. Hit a linter error, and it's noticed before you have. You don't explain context to Cascade — it's already there.
But that awareness stops at the boundary of your machine. Cascade sees your Stripe integration code but not your Stripe data. It sees your HubSpot sync logic but not your actual contacts. It can tell you there's a bug in your webhook handler but not whether that bug is hitting customers right now.
We wanted Cascade's contextual awareness to extend past the filesystem. Not just "read the code" but "read the code, check the live data, and fix both at once."
Every AI platform supports MCP tools. But what happens when the tool you need doesn't exist yet? Maybe you're working with an internal API, a niche SaaS, or a service that nobody has wrapped in MCP. Until now, you'd write a custom server.
Now your AI can do it itself.
MCPBundles has always worked as an MCP server. You add it to Claude Desktop, Cursor, ChatGPT, or any MCP-compatible client, and your AI gets access to Stripe, HubSpot, Postgres, PostHog, Gmail, and every other service you've connected — with real credentials, real permissions, and real data.
The MCPBundles CLI is an alternative way to access those same tools. Instead of configuring MCPBundles as a remote MCP server in your client, you install a command-line tool and authenticate with an API key. The AI agent discovers and calls your tools through shell commands — the same 10,000+ tools, the same credentials, the same workspace permissions.
pip install mcpbundles
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.