9 posts tagged with "CLI"

TL;DR​

~22s → ~0.4s per production call on the MCPBundles CLI (May 2026, my Mac). Five calls back-to-back: ~110s → ~2.2s. The MCP slice of make growth-refresh-report: 15+ min → ~53s.

I run MCPBundles. I'm biased. I'm also the person who kept running a fifteen-minute Makefile every morning and telling myself that was fine because "most of it is Playwright anyway" — which was sometimes true and often a cope.

The breaking point wasn't a benchmark chart. It was last Tuesday. I ran time mcpbundles call … on a Postgres pull we'd used in growth scripts for months because I didn't believe the numbers our benchmark script printed. 0.38s wall clock. I re-ran it thinking I'd mistyped --as. Same answer. I'd been eating ~22s of local CLI overhead on every call. Not Gmail. Not the Hub. The sidecar boot path. I'd normalized it because the alternative was rewriting a Makefile I'd already rewritten twice.

The terminal is having its best year since the invention of cloud infrastructure.

Every major AI lab shipped a coding agent CLI. Every major SaaS company shipped or meaningfully updated a service CLI. And a new category is emerging — CLIs that connect the two, giving your coding agent access to production services without leaving the terminal.

We've been running MCPBundles for over a year — a platform where teams connect AI agents to production APIs. We built a CLI because we kept watching agents context-switch between writing code and needing to call Stripe, query a database, or check analytics. This guide covers everything worth installing in 2026, organized by what it actually does for you.

OpenClaw — the open-source personal AI assistant with over 340,000 GitHub stars — now supports MCP. That means it can connect to any MCP server and use external tools as first-class capabilities. Your personal AI can go from answering questions to actually doing things across your services.

We've been watching OpenClaw's growth since late 2025. It runs on your hardware, works with Claude, GPT-4, Gemini, DeepSeek, and local models through Ollama, and connects to 20+ messaging platforms — WhatsApp, Telegram, Discord, Slack, Signal, iMessage. It already had 3,200+ skills on ClawHub. What it didn't have was a standardized way to reach the services those skills talked about.

MCP changes that. And MCPBundles makes it trivial.

There's a war happening on Reddit right now, and it's getting heated.

On one side: developers who believe the Model Context Protocol is overengineered middleware — that AI agents should just call gh issue create and curl like any terminal user. On the other: engineers running MCP in production who say the skeptics will inevitably reinvent every feature MCP provides, just worse.

Both sides are partially right. But the debate itself is framed wrong.

I spent the last day using our MCPBundles CLI to search Reddit via MCP tools — browsing posts, pulling comment threads, analyzing arguments — all through authenticated MCP tool calls executed from the command line. The irony was not lost on me: I was using CLI to call MCP to read arguments about whether we need MCP or CLI.

The answer, as it turns out, is both. But not in the way most people think.

Claude Code ships with built-in MCP (Model Context Protocol) support. Add external tools — databases, APIs, SaaS platforms — directly from the terminal with claude mcp add. This page covers every command, config option, and common fix.

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.

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