MCP servers

If you are hunting for the best MCP servers for AI agents, or you just want to know what MCP servers are, here is the operator view. I run MCP servers in my daily stack, so this is not a spec summary from someone who read the docs once. GetAgentTools lists only what I actually use, with a verdict and no sponsored placements. Verdict first, then the detail.

Verdict: RUNNING. In one line: one protocol to wire agents into your tools.

What MCP servers are and how they work

MCP is an open-source standard for connecting AI applications to external systems. The official docs compare it to a USB-C port for AI: a single, standard way to plug an agent into data sources, tools, and workflows, instead of a different custom cable for every app.

The design is client-server. Your agent connects as a client, and each tool you want to reach runs as a server. The two talk over the protocol, so the agent can list what a server offers and call it: read a file, query a database, open a ticket, send a draft. One agent can hold many servers at once, and one server works with any client that speaks MCP.

That last part is the point. Because the protocol is shared, a server built for one agent works with the next one. The official site lists support across Claude, ChatGPT, VS Code, and Cursor, among others. Build once, connect anywhere.

MCP server examples: the reference servers

The clearest MCP server examples are the official reference servers. Each one wraps a single capability and starts with one command, which is why they are the first thing I point people at:

• Filesystem
• Git
• Fetch
• Memory
• Time
• Sequential Thinking
• Everything (a test server)

On top of those, companies publish servers for their own platforms, and a large community catalog fills in the rest. The official Claude Code docs show servers in action for tools like GitHub, Slack, Sentry, Postgres, and Figma. Ad platforms are joining too: Meta ships an MCP endpoint for ads, with the caveats I cover in the Meta Ads API review.

Why MCP servers are RUNNING in my stack

This is where agent stacks are heading, and I am already there.

My reason is plain: design files, analytics, email, hosting. One standard instead of custom glue per app. Before MCP, wiring an agent to each of those meant writing and maintaining a separate integration for every tool, then redoing the work for every new agent. With MCP I run a server per tool once, and every agent I use can reach all of them.

Take design files. The Figma server is the clearest case in my stack. Before it, turning a design into code meant reading the file by eye and copying specs, tokens, and spacing across by hand, one screen at a time. Now the agent reads the Figma file directly through the server and builds from it. This site was designed that way, and that server now has its own review.

That is what earns the RUNNING label. It removes the part of agent work I disliked most: the bespoke connector that breaks when an API shifts and has to be rebuilt per app. Now the design app, the analytics tool, the email platform, and the host each sit behind a server, and the agent picks them up as tools. When I add a new agent, the plumbing is already done.

I do not treat MCP as finished. The protocol is young and server quality varies. But the direction is set, and the tools I depend on already speak it.

Who MCP servers are best for

Connecting agents to an existing stack. If you already run a set of tools and you want your agents to act inside them rather than beside them, MCP is the cleanest way to wire it. The more tools you have, the more the one-standard math pays off.

Picking for an autonomous agent is a different job from picking for chat, because nobody reads the middle of an agent run. The criteria I use, tool count, description quality, error text and blast radius, are in choosing an MCP server for AI agents.

MCP servers by use case

Buffer and HubSpot below are vendor examples of where the protocol has landed, not entries from my own connected set.

MCP servers for social media

Running several social accounts by hand means repeating the same drafting and scheduling work on every platform. Buffer runs an official MCP server at mcp.buffer.com/mcp, included on every Buffer plan including Free, so an agent can read your connected channels, create and schedule posts, manage the queue, and pull post analytics back without a custom integration per network. It is the client-server pattern described above. Buffer runs the server, and any client that speaks MCP can reach it.

I have let an agent publish to a social network through my own browser. The notes from it include a click on Post that reported success without posting, and a composer that ate the text: four failures in eight attempts on one thread. My order for publishing is API first, browser click last. A server like Buffer's is the API route, and it still goes through the vetting section further down this page before it touches a real account.

MCP servers for business tools

Ad platforms already appear above through the Meta Ads MCP endpoint, which the Meta Ads API review covers. CRM is the entry this page has not covered yet. HubSpot's remote MCP server left beta and became generally available in April 2026. It gives an MCP client read and write access to CRM objects such as contacts, companies, deals and tickets, plus read access to campaigns and landing pages. Every action runs under the HubSpot permissions the connecting user already has. The appeal matches every other server here: one connection instead of a custom integration per business tool, so the same agent that drafts a social post can also read a deal record.

Write access to a CRM is the sharper end of the vetting checklist further down this page. The nearest case in my own stack is an ad-platform server I wrote: fourteen tools, four of which can spend money, and anything it creates starts paused until a person turns it on. A CRM write lands on the live record directly, so the credential and reach step matters more here than almost anywhere else on this page.

MCP servers for coding

This is the category the reference server list further up this page already covers without naming it. Git handles version control from the reference set, and the Claude Code docs add GitHub, Sentry, Postgres and Figma, which between them give an agent the repository and its issues, the error stream, the database and the design file behind one protocol. Of those four, Figma is the one I keep connected, and what it changed is covered further up this page. Wiring those tools through MCP means one protocol for the whole loop instead of a separate integration per tool.

How I vet an MCP server before it stays connected

I vet an MCP server by connecting it in a sandbox, listing its tools, and reading every description the way the model will read it. Then it gets one real job with an answer I know. It stays connected only when the output is right, the path is short, and its worst action is one I can live with.

The checks, in the order I run them:

1. Source and version. Official or verified distribution, version pinned, and I know what an update can change before it changes it.
2. Tool list and descriptions. Fewer tools with sharp descriptions beat a long menu; a vague description is a wrong call the agent will make on my budget.
3. One correct call, one wrong one. I keep the error text, because an agent recovers from a clear error and loops on a cryptic one.
4. Credential and reach. A read-only key when the job is read-only, and a list of what it can write, delete, send, or pay for.
5. A real task, checked by hand. A job whose result I can verify without the agent; a right answer reached the long way round still fails.

Why the tool list and the error text weigh so much in an autonomous run is in MCP server for AI agents. The twelve-question connection review I run before anything with write access goes live is in MCP server security; if I cannot answer one of them, the server stays in the sandbox.

When to skip MCP and call the API directly

You automate a single app. If your whole use case is one tool and one script, a direct API call is simpler and has fewer moving parts. MCP earns its keep when there are many tools and many agents to connect. For a single app with a single job, the protocol is overhead you do not need yet.

The sharpest version of this is data access. Reads through a server are worth it, the write path in my own stack deliberately is not, and the reasoning is in connecting an MCP server to a database.

How much do MCP servers cost?

MCP is free and open-source, and most reference and community servers cost nothing to run. You pay only for whatever the server connects to, such as a hosting plan or a paid API behind it. There is no license for the protocol itself.

How to set up your first MCP server

An MCP server runs as a small process that exposes one tool's capabilities. Your agent connects to it as a client, reads the list of things the server can do, and calls them during a task. Servers can run locally on your machine or remotely over the network, and that choice moves your credential and your failure mode. If the host, client and server split is still fuzzy, or you want to know where your data ends up in that chain, read what an MCP server is in AI first.

Starting is quick if your agent already supports MCP:

1. Pick a server for a tool you use. The official reference servers (Filesystem, Git, Fetch, Memory) are the easiest first step, and many vendors ship their own. For what a working set looks like once the novelty wears off, see the list I keep connected.
2. Add it to your agent's MCP configuration. Reference servers run with a single command, for example npx -y @modelcontextprotocol/server-memory for the memory server.
3. Give the agent a task that needs that tool. It will call the server on its own instead of waiting for you to paste data in.

If you want your own tool exposed, you can build a server with the official Python or TypeScript SDKs. The docs walk through a first server end to end. What they leave out, the tool descriptions and the safe default for anything irreversible, is in writing your own MCP server.

MCP servers vs custom integrations

The honest comparison is not another product, it is the old way: writing a custom integration per app. That path means one connector for each tool, multiplied by each agent, and every one of them is yours to maintain. The official name for the problem MCP solves is the M by N integration problem, and it is real. I lived it.

MCP replaces that with one protocol on both sides. It sits next to your agents, since Claude Code, ChatGPT, Cursor, and VS Code all speak it, and next to your tools, and it lets the two meet without bespoke wiring. If you already run an agent, MCP servers are how you give it hands.

They also feed live data into custom workflows like my AI SEO stack. Browser tools such as Playwright use the same pattern: one server, a clear capability, and an agent that decides when to call it.

Automation platforms speak it too, in both directions. n8n can call external servers from an agent node and expose its own workflows as a server, so an automation you already built becomes a tool your agent can use.

MCP servers FAQ