4 posts tagged with "guide"

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.

The Glama directory lists 22,775 MCP servers as of May 2026. Most are weekend projects. Some are brilliant. BlueRock Security found 36.7% of public MCP servers carry SSRF vulnerabilities, 41% have no authentication at all, and only 8.5% use OAuth — so a "list of every MCP server" is not a useful list.

This guide is the opposite: the ~80 servers that real teams run in production, grouped by job. Four to five of them will cover 80% of what you ask your AI to do. We've been running MCPBundles for over a year — a platform where teams connect their AI agents to production APIs — and have tested, wrapped, and maintained MCP servers for hundreds of services. This is what we've learned about which ones are worth your time, what to skip, and why.

Following our prior post on wiring up an MCP server for our Django app — see How We Integrated Model Context Protocol (MCP) into Our Django App — we went back and revisited the architecture. "Too many tools" is still a huge problem for LLM productivity, which has continued into GPT5 and the latest Claude models so probably won't be solved toon. Cursor and Claude both work better when they have fewer tools to choose from, and our original setup exposed too many single-purpose GET tools. So we consolidated everything into a single, strongly-typed batch tool.

The result: one get tool, clearer schema, faster concurrent fetches, and less model confusion.

MCPs work like magic. Internally we use them relentlessly inside Cursor, for Linear issues in particular. We decided to ship an MCP server with MCP Bundles mainly because it made sense for us to have it on our own product for testing, before we even provided it to our customers. We built it quickly and made choices-of-least-resistance so there may be better ways to do everything. This is why we wanted to share our experience, would love to hear your feedback.

So the headline is we decided to implement an MCP server, Model Context Protocol (MCP), in our Django application, built on top of our existing API endpoints, and get it working with Cursor and Claude 3.7.