Claude vs ChatGPT vs Gemini: How Each Implements MCP Differently

Same spec, three incompatible builds

Claude, ChatGPT, and Gemini all speak Model Context Protocol. None of them run it the same way. A server written for one often needs a rewrite before the other two will touch it — a distinction that decides which client an engineering team should target first before writing a single tool definition.

MCP itself has grown quickly since Anthropic first shipped it. Monthly SDK downloads across the Python and TypeScript SDKs went from around 2 million at launch in November 2024 to more than 97 million by March 2026, according to a comparison of the three vendors' implementations. In December 2025, Anthropic donated the protocol to the Agentic AI Foundation under the Linux Foundation, with OpenAI, Google, Microsoft, and AWS backing it — the move that turned MCP from an Anthropic project into a neutral standard. That neutrality is exactly why the divergence below matters: nobody owns the spec, but everyone interprets the transport, rendering, and governance layers on top of it differently.

Claude: local-first, stdio pipes

Claude's MCP servers run locally, in the desktop app or the terminal, over stdio. There is no remote hosting step and no network hop between the client and the tool — the server is a process the desktop app spawns and talks to directly.

What this means for your roadmap

Building for Claude first means shipping a binary or script, not a hosted endpoint. Distribution runs through local install — an npm package, a binary download, or a config file pointing at a command — rather than a URL. That is cheap to prototype and easy to debug, since you can attach a terminal and watch stdin and stdout directly, but it does not scale to a hosted, multi-tenant product without a separate remote-serving layer bolted on later.

ChatGPT: remote-only, rendered in an iframe

ChatGPT supports remote MCP servers only, reached over streamable HTTP. No local servers, no stdio. Every tool call it makes crosses the network to a server you host and keep running.

ChatGPT also extends MCP with the Apps SDK, so tool output renders as inline interactive widgets — cards, charts, forms, data tables — shown in an iframe inside the conversation rather than as plain text. That turns a tool call into a small embedded application, at the cost of building and maintaining that UI layer yourself.

What this means for your roadmap

Targeting ChatGPT first means operating a hosted service from day one: authentication, uptime, and a streamable-HTTP endpoint, plus, if the richer surface is worth it, an iframe-rendered widget on top of the tool response. There is no local fallback to prototype against; the remote server is the only path in.

Gemini: governed, enterprise-first

Google's public MCP footprint is smaller and more tightly bounded than the other two. Google reported more than 50 managed MCP servers, with a cap of 100 enabled actions per custom server, according to the same comparison. The emphasis sits on governance rather than raw server count.

That governance framing shows up more explicitly in a separate enterprise MCP gateway guide, which claims MCP has crossed 78% adoption among production AI engineering teams and that its own public registry has passed 9,400 servers — a count close to, but not identical to, the 10,000-server figure reported elsewhere. Figures like these use different counting methods per vendor — a curated connector directory, an app store, a managed-server count — so the gap between 10,000 and 9,400 looks like a methodology difference rather than a contradiction; read both as scale, not a like-for-like scoreboard.

The same gateway guide argues that a bare MCP proxy is roughly 5% of the actual scope teams need, with the rest being what makes a deployment usable, governed, and defensible to a security team. As an example, the guide describes an on-behalf-of (OBO) identity pattern: without OBO, an audit log records “gateway service account called database write tool”; with OBO, it records “Elena Mwangi in Finance called database write tool at 14:32 UTC.” Whether that level of identity-aware auditing is necessary depends on whether the team shipping the server answers to a compliance function — this is a governance guide's framing of what enterprise deployment requires, not a claim about how Gemini itself is built.

What this means for your roadmap

A Gemini-first build is really an enterprise-first build. The action-count cap and the governance emphasis both point toward per-user identity, audit trails, and access review as first-class requirements — decided before the tool ships, not bolted on after a security review flags it.

Side by side

A complication: MCP Apps blur the split

The three-way split above is not airtight. MCP Apps — a provider-agnostic open standard for embedded UIs, similar in spirit to ChatGPT's Apps SDK — run inside iframes and communicate with any compatible host, including Cursor, Claude.ai, and ChatGPT, over a shared bridge. The architecture uses ui/* JSON-RPC over postMessage, which lets a single UI work across hosts without platform-specific integration code.

That matters because it means the iframe-rendering pattern will not stay exclusive to ChatGPT: a UI built to the MCP Apps standard is a bet on portability across clients, not lock-in to one.

An illustrative config contrast

The practical difference between the Claude and ChatGPT paths shows up in how a server is declared, not in the tool logic itself. The snippet below is illustrative only — it does not reproduce any vendor's actual configuration schema, only the shape of the difference:

// Illustrative: local stdio server (Claude-style)
{
  "command": "node",
  "args": ["./mcp-server.js"]
}

// Illustrative: remote streamable-HTTP server (ChatGPT-style)
{
  "url": "https://your-server.example.com/mcp",
  "transport": "streamable-http"
}

Which one to build for first

Pick Claude first if the goal is a fast prototype, a developer-facing tool, or something that never needs to leave a single machine — stdio removes hosting cost and network latency from the equation entirely. Pick ChatGPT first if the payoff is a rich, embedded UI in front of a large existing user base, and the team is prepared to run and secure a hosted endpoint from day one. Pick Gemini first, or budget for it early regardless of which client comes first, if the buyer is an enterprise security team — the action caps and identity-aware auditing described above are not optional there.

Building for only one client is the cheaper path in the short term. Building for all three means treating transport and rendering as a pluggable layer above the same tool logic, since the tool definitions are the one part of the spec that stays genuinely portable. The gap Anthropic, OpenAI, and Google each leave unfilled is the layer they built alone: how the server gets reached, what the response looks like, and who has to sign off before it goes live.