83 posts tagged with "AI Agents"

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.

Yes, Windsurf supports MCP. Cascade — Windsurf's AI agent — can connect to external MCP servers to access databases, APIs, and SaaS platforms directly from your IDE. This guide covers setup, configuration, known limitations, and how to work around the 100-tool cap.

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.

When you're building MCP tools, there's a moment where you realize something counterintuitive: the description field isn't just documentation—it's instruction. Every parameter description you write is a teaching moment where the AI learns not just what a parameter is, but when to use it, why it matters, and how it impacts the operation.

This shift in thinking—from documenting to teaching—changes how you design tools. Let me show you what that looks like in practice.