How to Choose MCP Servers for Your Team

The wrong way most teams start

A developer hits a wall, searches "best MCP servers," and installs three or four from a ranked list. Six months later the team has a dozen MCP servers connected to Claude Code or Cursor, no one remembers which ones are actively maintained, and a security review turns into an afternoon of reading unfamiliar GitHub repos to figure out what each one can actually touch.

This is the same failure mode SaaS sprawl caused a decade ago, compressed into months instead of years. MCP makes it trivially easy to connect a tool. It does nothing on its own to make that connection accountable, which is exactly the gap that shows up when someone finally asks who has access to what. Red Hat's own analysis of MCP security risks makes the same point from the infrastructure side: the protocol standardizes the connection, not the governance around it.

The five things worth checking before you add one

1. Who maintains it, and how often

An MCP server is code with access to your data. Check the commit history before the feature list. A server with no commits in four months and open, unaddressed issues is a server nobody is patching if something breaks or a vulnerability lands in one of its dependencies.

Official vendor-maintained servers, GitHub's own server among them, clear this bar by default. Community servers vary enormously. Some are maintained better than official ones. Many are abandoned once the author's own use case shipped.

2. What scope of access it actually requests

Read the auth flow, not just the README. A GitHub MCP server that requests repo-wide write access when your use case only needs to read issues is asking for more than it needs. The Model Context Protocol's own security guidance recommends scoped, tool-level permissions, but not every server implementation applies them carefully, and researchers have documented real "confused deputy" style exploits where a server is tricked into using its own elevated access on a user's behalf.

This matters more than it sounds like it should, because MCP servers are typically installed by individual developers on their own machines, outside whatever access review process governs the rest of your SaaS stack. A server with broad write access to your CRM, installed on one laptop, is a real gap in an otherwise reasonable security posture.

3. Whether it's a local process or a governed remote service

Local (stdio) servers run on the user's machine and typically use whatever credentials are already configured there. Remote, hosted servers run centrally and can be governed the way you'd govern any other piece of infrastructure: access logs, centralized credential rotation, and one place to revoke access when someone leaves. For anything beyond a single developer's personal tooling, a remote server is the easier thing to actually secure.

4. Whether it gives you one connection or one more silo

This is the part most comparisons skip. Installing ten single-purpose MCP servers means your AI tool now has ten separate connections, each with its own auth, its own failure modes, and no shared understanding between them. Ask your AI assistant a question that spans two systems, for example whether what you promised a customer in Slack matches what's logged in Salesforce, and a pile of individual MCP servers can't answer it, because each one only knows its own system.

A unified MCP layer solves this by presenting many data sources through one connection with shared context. Coworker MCP works this way: one connection gives Claude Code, Cursor, ChatGPT, Windsurf, or any MCP-compatible client access to 50+ connected apps, Slack, Jira, Salesforce, GitHub, and Google Drive among them, searchable as one system rather than ten separate ones, plus access to Coworker's own scoped agents for tasks that go beyond search.

5. What happens when it breaks

Ask what the failure mode looks like. A single-purpose community server going down is a minor inconvenience if it's one of ten. If your team has consolidated onto fewer, better-governed connections, an outage is more visible and gets fixed faster, which is usually the better trade for anything a team actually depends on daily.

One server per tool, or one layer for all of them

A simple rule of thumb

If the MCP server you're evaluating touches customer data, financial systems, or write access to anything customers can see, treat it like you'd treat a new SaaS vendor: check who maintains it, read what access it requests, and prefer a governed, centrally-managed connection over a scattered set of individually-installed ones. If it's a personal productivity tool with no write access and no sensitive data, the bar can reasonably be lower.

Embrace the Red's technical writeup of real MCP exploits is worth reading before rolling this out to a team larger than one or two people. Most of the documented issues come from the exact pattern above: a server with more trust and access than the use case actually required.

Where Coworker MCP fits

Coworker MCP is built around the governance gap above: one connection instead of a dozen, SOC 2 Type II and GDPR compliant, works with any MCP-compatible client, and gives your AI tools unified context across 50+ connected apps instead of ten separate silos that don't talk to each other. It's the difference between assembling your own stack of community servers and adopting infrastructure that's already built, maintained, and secured for you. It also plugs into the same context engineering discipline your team is probably already thinking about: unified access is only useful if what gets retrieved is actually relevant, which is where AI agent memory and retrieval quality come in.

See how Coworker MCP works, or book a demo to walk through your specific stack.

Frequently asked questions

What's the difference between an MCP server and an MCP client?

The server exposes data or tools, such as Slack messages, a CRM, or a file system, through the Model Context Protocol. The client is the AI application, Claude Code, Cursor, ChatGPT, or Windsurf, that connects to one or more servers to use that data. A single client can connect to many servers at once.

Are community MCP servers safe to use?

It depends entirely on the specific server. Check maintenance activity, the scope of access it requests, and whether it's a well-known vendor-maintained server or an individual's side project. There's no blanket answer; treat each one as a new piece of infrastructure with its own access to evaluate.

Do I need a different MCP server for every tool?

No. A unified MCP layer like Coworker MCP connects to 50+ apps through a single connection, so you don't need to install, maintain, and secure a separate server for every data source your team uses.

Can I use Coworker MCP alongside servers I've already set up?

Yes. Coworker MCP works with any MCP-compatible client and doesn't require removing other servers you're already running. Most teams use it to consolidate the sources that would otherwise need several individual servers.

How is this different from MCP vs. a traditional API integration?

An API integration is typically built once for a specific purpose and reviewed by an engineering team before it ships. An MCP server is often installed ad hoc by individual users, which is faster to adopt but skips that same review by default. See our full MCP vs. API breakdown for the mechanics.

Related reading

• What Is MCP? A Complete Guide
• MCP vs API: What Changes When You Standardise the Connection
• MCP Security: The Risks, and What Actually Mitigates Them
• RAG vs MCP: What's Actually Different