MCP vs Custom GPT Actions: Two Ways to Extend AI

TL;DR: MCP is a versioned, open protocol that any client and any server can implement, so one integration reaches Claude Desktop, Claude Code, Cursor, Codex, and OpenAI's own Responses API alike. A Custom GPT Action is a ChatGPT feature scoped to one GPT, and as of 2026 only Business, Enterprise, and Edu workspaces can even create one. Reach for MCP when portability matters; reach for Actions only if you already live inside a gated GPT workspace.

What problem are both of these actually solving?

Every capable model eventually needs to reach past its own training data: call a real API, read a live document, query a database, or trigger a workflow in another system. MCP and Custom GPT Actions both exist to give a model that reach, and both work the same basic way underneath: the model decides, mid-conversation, that calling a defined function would help, and the calling application executes it and hands the result back.

Where they differ is the layer each one operates at. MCP is a specification: an open, versioned contract for how any client and any server exchange tools, independent of who built either side. A Custom GPT Action is a feature of one product: it lives inside a single GPT you configure inside ChatGPT's builder, and it exists nowhere else. That difference in altitude, protocol versus product feature, is the entire comparison, and it is worth understanding in detail before you build against either one.

Picture the same underlying need built two ways: a company wants an AI assistant that can look up a customer's order status from an internal system. Built as a Custom GPT Action, that lookup lives inside one GPT, reachable only by whoever has access to that GPT inside that ChatGPT workspace, and only from ChatGPT. Built as an MCP server instead, the same lookup logic is exposed once, and becomes reachable from a support agent's Claude Desktop, an engineer's Claude Code session while debugging a related ticket, and any other MCP-aware tool the company adopts next year, all without touching the server again. The Action is not a lesser design; it fits the case where an internal API only ever needs to be reachable from ChatGPT. The moment more than one kind of client needs the same capability, the two approaches stop being equivalent.

Who can actually build a Custom GPT in 2026?

Fewer people than almost anyone assumes. OpenAI's own help center is unambiguous: "Personal ChatGPT accounts, including Free, Go, Plus, and Pro, cannot create or publish new GPTs. Existing GPTs remain available to use, and users can continue editing GPTs they previously created if their plan and permissions allow it" (help.openai.com/en/articles/8554397-creating-a-gpt, accessed September 3, 2026). The same page states who is left: "Business, Enterprise, and Edu workspaces can create, edit, and publish GPTs when their workspace settings and permissions allow it."

That is materially stricter than "requires a paid plan," which is the assumption behind a great deal of still-ranking content about GPT building. A Plus subscriber, on the individual $20-a-month plan, cannot create a new GPT at all today, full stop, regardless of what they pay. Only a managed workspace, with an admin who has turned the capability on, can.

What exactly is an MCP server, in plain terms?

The Model Context Protocol's own specification frames it as connecting AI applications to outside context and tools: "Whether you're building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need" (modelcontextprotocol.io/specification/2026-07-28, accessed September 3, 2026). It defines three roles: a host application that starts the conversation, a client inside that host that speaks the protocol, and a server that exposes capabilities. Those capabilities come in three flavors, tools the model can call, resources the application can attach to context, and prompts a person explicitly selects. Our sibling post on MCP tools, resources, and prompts covers that split in depth; this post stays at the architecture level.

MCP also standardizes how the wire connection itself works, which matters once you are actually configuring a server. The spec names two standard transports, stdio and Streamable HTTP. It defines stdio as "newline-delimited messages over the standard streams of a client-launched subprocess", and Streamable HTTP as one where "each message is an HTTP POST to a single MCP endpoint; replies arrive as a JSON object or a request-scoped SSE stream" (modelcontextprotocol.io/specification/2026-07-28/basic/transports, accessed September 3, 2026). A local tool typically runs over stdio; a hosted server like ours runs over Streamable HTTP.

How does a Custom GPT Action actually work under the hood?

An Action connects one GPT to one external API, defined entirely inside that GPT's configuration. OpenAI's own documentation is direct about the mechanism: "Each action is defined by two main components: how the GPT authenticates with the API, and a schema that defines what the API can do." It adds, "The schema must be provided as an OpenAPI specification in JSON or YAML" (help.openai.com/en/articles/9442513, accessed September 3, 2026). Authentication is one of three options set per Action: none, an API key, or OAuth with a client ID, secret, and token exchange.

Two constraints are easy to miss until you hit them. First, "A GPT can use either apps or actions, but not both at the same time" (same page), so adding one forecloses the other on that GPT. Second, a workspace can restrict which API domains an Action is even allowed to call: "If your workspace allows zero action domains, GPT custom actions cannot execute because no action domain can pass allowlist checks" (same page). An Action is real engineering, an OpenAPI schema and an auth flow, but all of it is scoped to the one GPT you configured it on.

What does actually setting one up look like, step by step?

The two setup paths do not resemble each other, which is itself a useful signal of how differently scoped they are. Standing up a Custom GPT Action, once your workspace permits GPT creation at all, looks roughly like this: open the GPT builder, choose Actions, paste or upload the target API's OpenAPI schema, configure one of the three authentication modes, test each operation in the built-in Preview pane, then save and, if the workspace allows publishing, submit the GPT for that workspace's own review. Every one of those steps happens inside ChatGPT, and the result is reachable only from inside that one GPT.

Connecting an MCP server looks closer to installing a browser extension than configuring an app feature. In a client like Claude Code, you run one command pointing at the server's URL; in Cursor or Claude Desktop, you add an entry to a config file or paste a URL into a settings panel. Either way, the client then opens a browser tab for an OAuth sign-in and consent screen, or accepts a personal access token for headless setups, and every tool the server exposes becomes available in every conversation that client starts from then on, with no separate configuration step per conversation and no per-integration schema to author, because the server already described its own tools when the client connected.

Does one integration reach every AI client, or just one app?

This is the load-bearing difference, and it is why the two are not really substitutes for each other. Configure an MCP server once, and every client that implements the spec can reach it, with no per-client rebuild. Configure a Custom GPT Action, and it works inside exactly the one GPT you built it in, and nowhere a person or another model happens to be working instead.

The clients themselves prove the point by disagreeing on where they even store the connection, which only makes sense if each is a separate, protocol-level integration rather than a shared product feature:

Four different storage formats, one shared protocol underneath. Codex's own documentation puts the format plainly: "Codex stores MCP configuration in config.toml alongside other Codex configuration settings. By default this is ~/.codex/config.toml" (learn.chatgpt.com/docs/extend/mcp, accessed September 3, 2026), and each server sits under a [mcp_servers.<name>] table, for example:

[mcp_servers.prompt_architects]
url = "https://mcp.prompt-architects.com/mcp"
bearer_token_env_var = "PA_TOKEN"

That same host also happens to be built by the company that ships Custom GPTs. OpenAI itself did not wire Custom GPT Actions into that config format, because Actions are not a protocol; there is nothing to standardize into a shared file.

The clearest evidence that MCP has out-run Actions on portability, even inside OpenAI's own stack: the Responses API, the surface OpenAI now recommends for new agent work, lets a request attach any remote MCP server directly. "Remote MCP servers require a server_url. Depending on the server, you may also need an OAuth authorization parameter containing an access token" (developers.openai.com/api/docs/guides/tools-connectors-mcp, accessed September 3, 2026). That means a developer building on OpenAI's own API can point at an MCP server, ours included, with no ChatGPT workspace, no ChatGPT account tier, and no ChatGPT-specific Action schema anywhere in the request.

What changed with the Assistants API shutdown, and does it touch this comparison?

Indirectly, but it is worth flagging so nobody builds against a dead surface. OpenAI's own migration guide says plainly: "The Assistants API was officially sunset on August 26, 2026, and is no longer available. Use the Responses API for new integrations" (developers.openai.com/api/docs/assistants/migration, accessed September 3, 2026), continuing, "Start new chats with the Conversations API and Responses API." Custom GPTs are a distinct ChatGPT product feature and were never built on the Assistants API, so they are unaffected directly. But the Responses API is exactly where OpenAI's mcp tool type lives, so any team choosing between "build an Action" and "build against MCP" for programmatic work is, in practice, also choosing which of OpenAI's own current APIs they build on.

Is a personal access token in a config file actually safe?

Only if you treat it like the credential it is. A pa_live_…-style personal access token, or any bearer token you paste into claude_desktop_config.json or config.toml, is a long-lived, plaintext secret sitting in a file that lives right next to your other project files, one careless git add . away from a public repository. Anyone who reads that file, a coworker with repo access, a leaked backup, a misconfigured CI log, can act as you against every tool the server exposes until the token is revoked.

MCP's own specification treats this seriously at the protocol level: "Authorization servers MUST implement OAuth 2.1 with appropriate security measures for both confidential and public clients" (modelcontextprotocol.io/specification/2026-07-28/basic/authorization, accessed September 3, 2026). Its broader security guidance goes further than authentication alone: "Hosts must obtain explicit user consent before invoking any tool". Separately, because a malicious server can describe its own tools however it likes, the same guidance warns that "descriptions of tool behavior such as annotations should be considered untrusted, unless obtained from a trusted server" (modelcontextprotocol.io/specification/2026-07-28, accessed September 3, 2026).

So which should you actually use?

If you are choosing for yourself as a builder, and you want one tool-calling integration that reaches Claude Desktop, Claude.ai, Cursor, Claude Code, Codex, and OpenAI's own API without being rebuilt per client, MCP is the only one of the two that was designed for that. If you already operate inside a Business, Enterprise, or Edu ChatGPT workspace and only need one API reachable from one internal GPT, an Action is a smaller, faster thing to stand up, and the OpenAPI schema you already have for that API is most of the work.

Our own MCP server is a working example of the portability argument, not just a claim about it: https://mcp.prompt-architects.com/mcp exposes four tools, reachable from Claude Desktop, Claude.ai, Cursor, Claude Code, Codex, and Codex CLI, using either OAuth 2.1 with PKCE or a pa_live_… personal access token for headless setups. The four are not one action with four names: improve rewrites a prompt for clarity, structure, and specificity for everyday polish; refine asks one to three clarifying questions before producing a tailored result, for a prompt that started vague; shorten compresses a prompt while preserving intent, for a context-window or character limit; and enhance is the heaviest of the four, adding structure, role-framing, and depth for a high-stakes prompt. One server, six clients, no per-client rebuild, and no OpenAPI schema to author before any of them can reach it.

If you are deciding what belongs in a shared prompt agent setup versus a one-off script, our post on context vs memory vs custom instructions covers the adjacent question of where configuration should actually live, and if you are evaluating prompt tooling more broadly, our three-way comparison of FlashPrompt, AIPRM, and Prompt Architects covers the extension side of the same decision.