What Is MCP? Model Context Protocol Explained

What is MCP? It's the Model Context Protocol, an open standard that lets an AI assistant like Claude, ChatGPT or Cursor call external tools and read external data through one common interface — instead of someone hand-building a separate integration for every assistant-and-service pairing. Introduced by Anthropic and now adopted across many AI applications, MCP is often described as a "USB-C port for AI applications": one shape that many things plug into. This article explains what it is, why it exists, how it works, and how to connect a server yourself.

• MCP is a standard, not a product. Nobody sells "an MCP"; it defines how an assistant and an outside service exchange requests and responses.
• Three terms to track: the assistant (host), the MCP client inside it, and the MCP server that plugs in and exposes tools and data.
• It replaces N×M custom integrations with N+M. Build one server per tool and one client per assistant, and any compliant pair can talk without custom glue.
• MCP, APIs and RAG work together. RAG brings knowledge in, MCP lets the model choose and take actions, and the underlying API does the real work.
• Security is your choice. A connected server sees only the scopes you approve; read them, prefer read-only, and never paste secrets into a prompt.

What Is MCP? A Plain-English Definition

Think about the phone in your pocket. It ships with a handful of built-in apps — a camera, a browser, a maps app — and when you need something it can't do yet, you install another app that plugs into the same phone. You don't rewire the hardware; the app just extends what the phone can do.

An AI assistant works the same way. Claude, ChatGPT and Cursor each ship with built-in abilities — they can write text, answer questions and reason about what you give them. But out of the box they can't see your calendar, query your company database or file a ticket in your project tracker. MCP is what lets you "install" those abilities, the same way you'd add an app to a phone.

So, what is MCP? MCP stands for Model Context Protocol. It is an open standard that lets an AI assistant call external tools and read external data through one common interface — instead of someone hand-building a separate, custom integration for every assistant-and-service pairing. It was introduced by Anthropic and has since been adopted across a range of AI applications and development tools. A useful shorthand you'll see repeated: MCP is like a USB-C port for AI applications — one shape that many things plug into.

That's the plain version of "MCP in AI" meaning: a shared plug so an assistant and an outside system can talk without a bespoke connector each time.

The three terms to track

You only need to hold three words in your head to follow the rest of this article:

• Assistant — the AI application you talk to, such as Claude, ChatGPT or Cursor. This is the "phone." (In MCP terminology this side is sometimes called the client or host; we'll use "assistant" for now.)
• MCP — the standard itself. It defines how the assistant and an outside service exchange requests and responses. It's a protocol, not a product; nobody sells "an MCP."
• MCP server — the thing that plugs in. Think of it as an app for your assistant: it exposes a specific service — your calendar, a database, a design tool — over MCP so the assistant can use it. One MCP server can be reused by any assistant that speaks the protocol.

A few more terms — tools, resources, and the way the assistant and server actually connect over the network — matter later, and we'll define each one the moment it first comes up rather than front-loading jargon here.

Because MCP is an open standard, its exact behavior is written down and versioned: it moves in dated revisions rather than shifting silently underneath you. The specification, along with guides for using and building servers, lives at modelcontextprotocol.io, and that's the primary source to check for the current revision — worth a look, since the standard is evolving quickly and any version detail you read elsewhere may already be out of date.

Why MCP Exists: The Problem Before It

To understand why MCP matters, it helps to look at what building AI integrations felt like before it existed. An AI assistant on its own is a closed box: it can reason over the text you give it, but it cannot read your calendar, query your database, or file a ticket unless someone wires it up to those systems. So every team that wanted a useful assistant had to build that wiring by hand.

The N×M integration problem

The trouble is that "the wiring" was never reusable. Each AI application spoke its own language for tools and data, and each tool exposed itself differently. If you had N tools you wanted to reach (Google Calendar, a Postgres database, Jira, an internal API) and M assistants you wanted to reach them from (one chat product, a coding assistant, an internal agent), you were on the hook for roughly N×M separate integrations. Every new assistant meant re-integrating every tool; every new tool meant re-integrating with every assistant.

This is the pattern people call the N×M problem. It shows up whenever many producers need to talk to many consumers without a shared contract. The work grows multiplicatively, and most of it is glue code that has to be maintained forever — authentication, data formatting, error handling, and versioning, rewritten slightly differently each time.

How MCP turns N×M into N+M

MCP — the Model Context Protocol, an open standard introduced by Anthropic for connecting AI applications to external systems — replaces that many-to-many mess with a single shared interface. Each tool implements the protocol once, as a server. Each assistant implements the protocol once, as a client. After that, any compliant client can talk to any compliant server without custom glue.

In other words, the integration cost drops from N×M to N+M: you do the work once per side rather than once per pair.

This is the same idea behind the common "USB-C port for AI applications" analogy: a single physical connector replaced a drawer full of proprietary cables, and any device that speaks the standard can plug into any other. Analysts at Databricks and the team at Anthropic have both framed MCP in these terms — a standard interface that collapses bespoke, one-off connectors into a reusable contract.

Why the standard, not just the code, is the point

A protocol is only useful if enough people agree to use it, and that is where broad ecosystem support changes the math. Because MCP is an open standard rather than one vendor's SDK, the same server you build can be reached by assistants like Claude and ChatGPT and by development tools such as Visual Studio Code and Cursor. You build once and integrate everywhere, instead of maintaining a separate connector for each destination.

That shift is why MCP matters differently depending on where you sit. If you build tools, you write one server and reach every compatible assistant. If you build assistants, you gain access to a growing catalog of data sources and actions without negotiating each one. And if you just use AI to get work done, you end up with assistants that can actually touch your calendar, your files, and your systems — because the integration cost finally stopped multiplying.

How MCP Works: Architecture in Plain Language

To understand how MCP works, it helps to see that the protocol splits the job across three parts. Each has a narrow, well-defined role, and that separation is what lets you build a tool once and use it in many assistants. This is the core MCP architecture: a client-server model with a host application sitting on top.

The three components

• Host — the assistant application you actually interact with, such as Claude, ChatGPT or Cursor. The host owns the conversation, decides when outside help is needed, and shows you the final answer.
• MCP client — a component inside the host that speaks the protocol. It opens a connection to a server, asks what that server can do, and sends requests on the assistant's behalf. Typically there is one client per connected server.
• MCP server — a separate program that exposes capabilities to the client. The two main capability types are tools (actions the assistant can call, such as "create an issue" or "run a query") and resources (data the assistant can read, such as a file or a database record).

A useful way to hold the mcp architecture and components in your head: the host is the brain, the client is the messenger, and the server is the thing that actually touches your data or systems.

One request, end to end

Here is how does MCP work in practice, traced through a single request. Imagine you ask an assistant, "What's on my calendar tomorrow?" and it is connected to a calendar server.

1. You ask. You type a request into the host.
2. The assistant decides it needs outside help. The model recognizes it can't answer from its own knowledge and that a connected tool — the calendar server — is a better source.
3. The client sends a request. The MCP client packages the tool call, along with any arguments the model supplied (for example, the date range), and sends it to the server.
4. The server responds. The server runs the action against the real system, then returns a structured result — the list of events — back to the client.
5. The assistant folds the result into its answer. The model reads the returned data and writes a plain-language reply for you, citing the events it found.

Every connected server follows the same pattern, which is why one host can juggle a calendar server, a database server and a design server without custom glue for each.

The transport layer

The client and server exchange messages over a transport — the channel that carries the protocol between them. MCP defines two:

• stdio — used when the server runs locally on the same machine as the host. The client launches the server as a subprocess and exchanges messages over standard input and output. This is common for local development and desktop tools.
• Streamable HTTP — used for remote servers reached over the network. It carries requests over HTTP and can stream responses back, which suits hosted servers you connect to over the internet.

Whichever transport is in play, the message format and the client-server model above stay the same — only the pipe changes.

MCP is an open standard, and its specification is versioned by a dated revision so hosts and servers can agree on which behaviors are supported. When you check compatibility between a client and a server, the spec revision each side implements is the detail to look at.

MCP vs API vs RAG: How They Differ

Once you understand that MCP (Model Context Protocol, an open standard for connecting AI applications to external systems) gives models a way to use tools, a fair question follows: how is that different from an API, or from RAG? The short answer is that they solve different problems and often work together. Comparing "mcp vs api" or "mcp versus rag" as rivals misses the point — you'll frequently use all three in the same workflow.

Two quick definitions so the table makes sense. An API (application programming interface) is a defined set of endpoints that lets one piece of software talk to another; it's the plumbing most web services already expose. RAG (retrieval-augmented generation) is a pattern where you search a collection of documents, pull back the most relevant passages, and paste them into the model's context so its answer is grounded in real content.

Notice the overlap: MCP servers are very often thin wrappers around existing APIs. The API is still doing the work underneath; MCP just presents it in a form a model can understand and select on its own, rather than requiring a developer to hard-code each call. And RAG can live behind an MCP tool — a "search documentation" tool is essentially RAG the agent can invoke when it decides it needs context.

A concrete example: they work together

Imagine a support assistant handling a customer message about a billing error.

1. RAG answers the "what" question. The assistant calls a search tool that retrieves the three most relevant help-center articles about billing, so its explanation is grounded in your actual policy rather than a guess.
2. MCP handles the "do" part. Once the assistant confirms the issue needs escalation, it calls an open a ticket tool exposed by an MCP server, passing the customer ID, a summary and a priority. The ticket is created in your helpdesk and a reference number comes back.
3. The API is what actually executes step 2. The MCP server's open a ticket tool is a wrapper over your helpdesk's existing REST API — the same endpoint a developer would call directly, now callable by the agent through a standard interface.

So the split is clean: RAG brings knowledge in, MCP lets the model choose and take actions, and the underlying API does the real work. You don't pick one — you compose them.

See It Work: A 3-Minute Live Demo With No Credentials

The fastest way to understand MCP is to watch an assistant use a tool for real. This demo uses a Time server — an MCP server that reports the current time in any timezone. It needs no login, no API key and no payment, so it is a safe first connection and a good "MCP for dummies" starting point.

First, ask before you connect anything

Open your assistant (Claude, ChatGPT or Cursor) in a fresh chat with no tools connected, and ask:

• "What is the exact current time in Tokyo right now?"

Watch what happens. A language model has no live clock, so it will do one of two things: guess (often stating a plausible but wrong time), or decline and tell you it cannot access real-time information. Either way, there is no tool call and no verifiable answer. Keep this response in mind — it is the "before".

Connect the Time server

MCP servers are added in your assistant's connector or MCP settings — the panel where the app manages external tools. The exact label differs by client, but the flow is the same:

1. Get the server's URL. You can find the Time server and copy its connection URL.
2. In your assistant, open the connectors / MCP settings panel and choose to add a new server or custom connector.
3. Paste the server URL. Because this server needs no credentials, there is no OAuth login step (OAuth 2.1 is the sign-in standard MCP uses when a server does need your account — here it is skipped).
4. Save and enable the connection. The client will connect and read the server's list of available tools; you should see a time-related tool appear as available.

Now ask the same question again

In a new message, ask the same thing:

• "What is the exact current time in Tokyo right now?"

This time the assistant recognizes it has a tool for the job. You will typically see an indication that it is calling the tool (a "using tool" or tool-call marker), then a precise answer with the correct timezone — data it fetched from the server, not from its training. That round trip is MCP explained in one screen: the model decided a tool was needed, called it, and used the real result.

The contrast is the point. Same question, same model — but the connected version returns an exact, sourced answer instead of a guess, because a real server did the work.

If the tool call fails

Two quick things to check before anything else:

• Is the connection actually enabled? Reopen the connectors / MCP settings panel and confirm the Time server shows as connected and its tool is toggled on. A pasted-but-not-saved URL is the most common cause.
• Did you prompt in a way that invites the tool? Ask a concrete, current-time question ("what time is it now in …") rather than a general one. If the model still answers from memory, start a new chat so it re-reads the available tools, and try again.

Connecting a Real MCP Server to Claude, ChatGPT and Cursor

Knowing what MCP is only gets you so far. The useful skill is wiring a server — a program that exposes tools and data over the Model Context Protocol — into the assistant you already use. The menus below move often, so treat this as a snapshot dated 2026-08-16 and confirm against each vendor's current docs before you follow along.

Two kinds of server exist, and they connect very differently:

• Local (stdio) servers run as a process on your own machine. You point the client at a command to launch, usually via a JSON config file. Good for developer tooling; nothing leaves your laptop unless the server itself makes a call.
• Remote (hosted) servers run somewhere on the internet and speak over HTTP. Instead of editing a config file, you paste a URL, and the client walks you through an OAuth 2.1 consent screen — the standard sign-in flow where you approve specific permissions (scopes) and the client receives a token rather than your password. This is the model most non-developers will use.

Claude

Claude supports MCP in both the desktop app and Claude Code. In the desktop app, connectors are managed under Settings, where you add a remote server by URL or enable a local one. Adding a hosted connector triggers the OAuth consent flow in your browser; you approve the requested scopes and Claude stores the token. Claude Code, the command-line tool, manages servers through its own config and CLI commands rather than the desktop settings pane.

ChatGPT

In ChatGPT, MCP-style connections appear as connectors and are exposed to the model through features like tools in the developer and business tiers. Availability differs by plan and changes frequently, so check what your account actually shows under Settings before assuming a capability exists. As with Claude, a hosted server connection is authorized through an OAuth consent screen rather than a local file.

Cursor

Cursor, an AI-first code editor, reads MCP servers from a JSON config. You add servers under Settings → MCP (or by editing the config file directly), then toggle each one on. This is also the most common answer to "what is an MCP server in VS Code": in VS Code and its derivatives, MCP servers are declared in a JSON configuration and surfaced to the built-in agent, so the setup pattern is nearly identical to Cursor's.

A local-server config snippet

For local (stdio) servers, the config usually looks like the example below. Getting started means adding one server block, saving the file, and restarting the client so it picks up the change:

• command is the executable the client launches.
• args are the arguments passed to it.
• env holds any environment variables the server needs.

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/Users/you/projects"],
      "env": {}
    }
  }
}

After saving, restart the client and open its MCP or connectors panel. You should see the server listed and its tools available to the model. If it does not appear, check that the command runs on its own in a terminal, that the path in args exists, and that you edited the config the client actually reads — many apps keep separate files per profile.

For a hosted server there is no JSON to edit and no secret to paste into a prompt. You add the server's URL, the client opens the OAuth consent screen, you review the scopes and approve, and the connection is live. That flow keeps credentials out of your chat history and lets you revoke access later without touching a config file.

Security and Privacy: What the Server Can See

The most useful mental model for MCP and security is a browser extension. An extension can only touch what you install and grant it access to — but once it's installed, it runs its own code and can act on whatever permissions you approved. An MCP server (the program that exposes tools and data to an AI client like Claude, ChatGPT or Cursor) works the same way. It sees only what its authors built it to expose, and only after you deliberately connect it. But a connected server is live code with real access, so it deserves the same caution you'd give any third-party plugin.

What a connected server can and can't see

By default, an MCP server sees nothing about you. It has no view into your other tools, your files or your chat history unless a specific tool is designed to read them and you granted that access. What it can see is:

• The arguments the AI client sends when it calls one of the server's tools — for example, the search term or the record ID.
• Whatever data the account you connected is allowed to reach, within the permissions you approved.
• Any text you type into the conversation that the model then passes into a tool call.

That last point matters: if you paste something into the prompt and the assistant forwards it to a tool, the server behind that tool receives it. Treat the prompt as a public channel, not a private note.

The four auth terms, one sentence each

• OAuth 2.1 is the current authorization standard that lets you grant an app limited access to your account without ever handing over your password.
• PKCE (Proof Key for Code Exchange) is an extension to OAuth that stops an intercepted authorization code from being reused by an attacker, and it's mandatory in OAuth 2.1.
• A scope is a named permission — like "read your calendar" or "create issues" — that defines exactly what the connected server is allowed to do.
• A token is the short-lived credential the server receives after you approve access; it stands in for your login and can be revoked without changing your password.

When you connect a server over OAuth, you're usually taken to the provider's own login screen — not the server's — and shown a consent page listing the scopes being requested. The server never sees your password; it only receives a token limited to the scopes you approved.

Practical hygiene

These few habits cover most of the real-world security considerations with MCP:

1. Only add recognized sources. Connect servers published by the company that owns the underlying service, or distributed through a marketplace or registry you trust. An unfamiliar server running arbitrary code with access to your data is the same risk as installing an unknown extension.
2. Read the scopes before you approve. On the consent screen, check that the permissions match what the tool actually needs. A note-taking tool asking for full account admin is a red flag — deny it and stop there.
3. Prefer read-only when you can. If a server offers separate read and write scopes, start with read access until you trust it, then grant more.
4. Never paste secrets into a prompt. API keys, passwords and access tokens don't belong in the chat. Anything you type there can be logged by the client and forwarded to a tool. Let OAuth handle credentials instead.
5. Revoke what you stop using. Because access rides on a token, you can disconnect a server from your account's app settings at any time without touching your password.

Done this way, connecting a tool is a deliberate, reviewable act with a narrow blast radius — you decide which server, which account and which scopes, and you can pull the token back the moment something looks wrong.

In Claude specifically, this model surfaces as Claude connectors: each one is an authorized MCP link with its own scopes, and we break down exactly what a connected app can and cannot see in a separate guide.

The MCP Ecosystem: Clients, Servers and Registries

MCP only matters because so many tools speak it. A client is the AI application you actually type into — a chat assistant or a code editor — and it's the client that connects out to MCP servers on your behalf. A server exposes data or actions (a calendar, a database, a design file) through the protocol. Because both sides agree on the same standard, you can build a server once and use it across many clients.

Support has spread across the major assistants and developer tools. The table below lists clients that document MCP support. Client support changes quickly, so treat this as a starting point and confirm the current state in each vendor's own documentation before you rely on it.

The practical upshot for a non-developer: you are not locked into one assistant. A server you connect to Claude today should work with another client that speaks MCP, because they follow the same protocol rather than a private, one-off integration.

Who governs the protocol

MCP was introduced by Anthropic as an open standard and released as open source, meaning the specification and reference code are public for anyone to read, use and contribute to. Anthropic later transferred stewardship of the protocol to a vendor-neutral foundation, so no single company controls its direction.

That governance detail sounds like inside baseball, but it matters even if you never write a line of code. When a standard is owned by one vendor, it can change to suit that vendor's business. When it sits with an open community under neutral stewardship, competing clients and server authors all have a say, and you are less likely to be stranded if any one company shifts strategy. The role of open source in MCP is what lets Claude, ChatGPT, Cursor and independent tools all implement the same thing without asking permission — and it's why the same server can serve them all.

Where servers come from

MCP servers reach you through two main routes:

• Open-source examples and community repositories. There's a public collection of reference and example servers you can read, run locally and adapt. This is ideal for learning how a server is built and for developers who want to self-host and control every detail.
• Hosted, maintained catalogs. These run servers for you, handle authentication and keep them updated, so connecting is closer to installing an app than to standing up infrastructure. If you'd rather browse ready-to-connect, hosted servers instead of cloning and running code yourself, B77's catalog is one such option.

The trade-off is the familiar one: self-hosting an example server gives you full control and full responsibility for updates and security, while a hosted catalog trades some control for maintenance and one-click setup. Neither is "correct" — pick based on how much operational work you want to own.

What Changes for a Team (and for a Company With an API)

Once you understand what MCP is, the interesting question is what it changes in practice. The answer depends on which side of the connection you sit on: a team adopting servers, or a company that already has an API and could publish one.

For a team: connecting a server is an admin decision, not a personal one

When someone connects an MCP server to a shared assistant, they are granting a tool the ability to act on data through that assistant. That is the same category of decision you already make for browser extensions or workspace apps: something external gets scoped access to your environment, and you decide whether to trust it.

Treat it the same way. Practical governance usually comes down to a few questions:

• Which servers are approved? Maintain a short allowlist rather than letting anyone wire up anything.
• What access does each one get? Match scopes and tokens to the job — read-only where possible, write access only where it is genuinely needed.
• Who can add a new one? Route new connectors through review instead of self-service for the whole team.

The reason to take this seriously: a single connector is a shared surface. If one server is misconfigured, compromised, or over-scoped, it does not affect just the person who added it — it affects everyone using that assistant at once. That concentration of blast radius is exactly why approval deserves real review, not a rubber stamp. The good news is that you are not inventing a new process; you are extending the one you already apply to third-party apps.

For a company with an API: distribution instead of a hundred integrations

Now flip the lens. If your company already exposes an API, MCP changes how that capability reaches users of AI assistants. Today the burden usually falls on your customers: each one has to read your docs, write glue code, and maintain an integration to make your API usable inside their tools.

An MCP server inverts that. You (or a partner) build your API into a hosted MCP server once, define the tools and scopes cleanly, and it becomes something an assistant can connect to directly. Instead of asking every customer to integrate, you ship one server and let it be discovered and connected. The work to build MCP apps around your existing endpoints is largely a design and packaging exercise — deciding which actions to expose as tools, how to name and describe them, and what auth model fits — rather than a rewrite of your product.

The trade-off to weigh honestly: when you build servers on top of your API, you take on responsibility for how those tools behave in an agentic context. Tool descriptions become part of the interface, scopes become a security boundary you own, and metering and versioning matter because assistants call your tools without a human reading every response. Done well, it turns "integrate with us" from a customer project into a connection your customers make in a couple of clicks.

Honest Limits: What MCP Does Not Fix

MCP (Model Context Protocol, the open standard for connecting AI assistants to external tools and data) solves the wiring problem well. It does not solve every problem people expect it to. If you understand what can go wrong and what's overhyped before you connect anything, you'll set better expectations for your team and avoid a few common misconceptions. The biggest one: MCP is not just another API framework that magically makes your data useful. Here are three limits worth knowing.

1. Context has a cost

Every tool an MCP server exposes has to be described to the model — its name, purpose, and parameters — so the assistant can decide when to call it. Those descriptions take up space in the model's context window and add discovery overhead. Connect one focused server and this is negligible. Connect many broad ones, each with dozens of tools, and the model has more options to sift through before it picks one.

In practice that can mean slower responses and less reliable tool selection: the assistant may call the wrong tool, or hesitate between similar ones. Caching hints and well-scoped tool sets help, but the honest rule of thumb is to connect the servers you actually need for a task rather than everything at once.

2. MCP standardizes the wiring, not the quality

MCP defines how a client and server talk to each other. It says nothing about whether the server behind that connection is any good. A clean, well-documented API with clear tool names and sensible parameters produces clean tool calls. A messy or poorly designed API produces messy tool calls — the protocol faithfully passes along whatever the server offers, warts and all.

So a bad integration doesn't become good just because it speaks MCP. Tool descriptions still need to be clear, error messages still need to be readable by a model, and the underlying data still needs to be accurate. When you evaluate a server, judge the tools it exposes, not the fact that it uses MCP.

3. Security depends entirely on what you install

MCP servers come in two shapes, and they carry very different risk. A local server runs on your own machine and inherits your machine's permissions — file access, network access, whatever your user account can do. If that server is untrusted or over-permissioned, so is anything it can reach. A remote server, by contrast, is limited to the scopes (the specific permissions) you approve during authorization, and it only ever sees the data those scopes allow.

The protocol itself doesn't decide whether a server is safe; your choices do. Read what a server asks for, prefer narrow scopes over broad ones, and be cautious about running local servers you didn't write or can't inspect. MCP makes connecting easy — it does not make an untrustworthy server trustworthy.

Frequently Asked Questions

Is MCP free?

The Model Context Protocol itself is an open standard, so the specification is free to read and implement — anyone can build a client or server against it at no cost. What you pay for is what sits behind a given server: an individual MCP might wrap a paid API, a subscription, or metered usage, and the AI client you use (Claude, ChatGPT, Cursor) may have its own pricing. So "is MCP free" splits into two answers: the protocol is free; specific servers and the data or actions they expose may not be.

Is MCP safe?

The protocol is designed to let language models connect to external systems in a controlled way, typically using OAuth 2.1 — an authorization standard that issues scoped access tokens instead of sharing your raw passwords. Whether a given setup is safe depends less on the protocol and more on the server you connect and the permissions you grant it, since a server can see the inputs your agent sends and act within the scopes you approve. To judge "is MCP safe" in practice, check who operates the server, what scopes it requests, and whether it stores your data — and avoid running untrusted servers with broad permissions.

Does an MCP server run locally or remotely?

Both are supported, and the "mcp local or remote" choice is up to whoever builds or hosts the server. A local server runs on your own machine — useful for accessing local files or tools — while a remote server runs on someone else's infrastructure and is reached over the network, usually with OAuth for authentication. Local servers keep data on your device; remote servers are easier to share and maintain but mean your requests travel to a third party, so the same security questions above apply.

Who makes MCP servers?

The protocol was introduced by Anthropic as an open standard, but the servers themselves come from a wide range of authors: the companies whose APIs are being exposed, independent developers, open-source communities, and marketplaces that co-build and host servers on a vendor's behalf. When asking "who makes MCP servers," it's worth checking the specific server's origin, because that tells you who is responsible for its security, updates and data handling. For any server touching sensitive data, prefer one operated by the API's owner or a trusted intermediary over an anonymous third party.

Do I need to code to use MCP?

No — using an MCP is different from building one. If you want to connect an existing server to an assistant like Claude, ChatGPT or Cursor, you generally add it through the client's settings and approve access, with no programming required. You only need to code for MCP if you want to build your own server to expose custom data or tools, and even then, hosted options and templates can reduce how much you write yourself.

Bringing It Together

MCP is best understood as a shared plug: one open standard that lets any compliant assistant talk to any compliant server, turning a tangle of custom integrations into a reusable contract. Once you've grasped the three components, traced a single request, and connected a no-credential server yourself, the rest is judgment — choosing which servers to trust, reading the scopes you approve, and connecting only what a task actually needs. If your next step is finding a hosted server to connect in a couple of clicks, or publishing your own API as one, B77's marketplace handles the hosting, auth and billing so you can focus on the work rather than the wiring.Browse hosted MCP servers and connect one to your assistant in a couple of clicks: the B77 marketplace.