Amit Kothari
Amit Kothari CEO of Tallyfy · Workflow AI Expert

How to get your MCP server listed everywhere in 2026

In brief

Getting an MCP server listed across AI platforms is not one process. Only Anthropic and OpenAI run a true public submit-review-publish pipeline; the rest are per-tenant connections, business-development deals, or self-publish registries. Build one hardened server to OAuth 2.0 and Streamable HTTP and you clear most of every program in 2026.

If you want your MCP server listed everywhere, the first thing to accept is that “everywhere” isn’t one place and “listed” isn’t one process. Across the major AI platforms, getting in works in four different ways. Two vendors run a real public pipeline where you submit, a human reviews, and you land in a directory users browse. The rest split three ways: connections a customer’s admin makes inside their own tenant, placements you negotiate through a partnership, and community registries you publish to yourself. Confuse those and you’ll waste weeks waiting for a review queue that doesn’t exist, or building a wrapping agent for a surface where a customer could have just connected you. This is the one-page map, and the order that actually pays off.

Summary

  • “Listed” means four different things - Only two surfaces run a public submit-review-publish pipeline. The rest are per-tenant connections, partner deals, or registries you publish to yourself, so the playbook changes per vendor.
  • Build the server once, reuse it everywhere - A remote HTTPS server on Streamable HTTP, OAuth 2.0 with user consent, annotated tools, a live privacy policy, and a demo account clears roughly 80 percent of every program’s bar.
  • Only Anthropic and OpenAI actually review and publish you - Everywhere else you connect inside a customer’s tenant, get invited through business development, or self-publish to a registry.
  • Start with the registry and Anthropic - One propagates your record across the ecosystem, the other gives the fastest real credibility. See what Tallyfy can run

There’s a second truth that works in your favor, and it’s worth saying up front. Most of these programs draw a hard line against pass-through middleware, a thin relay to someone else’s API. If your server connects to your own product, you read as first-party, and that whole objection never lands. Say so plainly in every listing.

Not every listing is the same kind of thing

Sort the surfaces by how they actually let you in, and the whole thing gets simpler.

Two of them run a true public pipeline. You submit your server, a person reviews it against published criteria, and if it passes, it appears in a directory real users browse. That’s Anthropic and OpenAI, and it’s the closest thing to a clean “apply and get verified” path that exists. The review is slow and quiet, but the rules are written down and the outcome is real.

The next group is self-serve, but per tenant. There’s no public directory you get into; instead, a customer’s admin connects your server inside their own workspace. Mistral’s Le Chat works this way for custom connectors. So does Gemini Enterprise, and so does Copilot Studio. You don’t get listed so much as you get connectable, and your job is to make that setup painless and documented.

Then there’s the business-development group, where the only door is a conversation. Google’s consumer Spark connectors, Mistral’s Featured directory, Google’s curated Enterprise connectors: none has a public form. You get in by knowing someone and bringing demand, or you don’t get in.

And underneath all of it sit the registries, which you publish to yourself with no review at all.

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That’s the map. A listing puts tools in front of an agent, but it never tells the agent which tool to use, in what order, or when to stop and ask a person. That judgment lives in the process you define, not the model you plug in, which is the argument running through the AI and future of work hub.

The build that every surface shares

Here’s the move that saves you from rebuilding for each vendor: construct one server to the strictest common bar, and you’ve cleared most of every program before you apply anywhere.

The shared spine is consistent across the review-based and self-serve surfaces alike. A remote, cloud-hosted server on a public HTTPS endpoint, not a local desktop build. Streamable HTTP as the transport, because the old standalone SSE option is deprecated and gets turned away. OAuth 2.0 with a real user-consent flow. Every tool annotated with a plain title and the right read-only or destructive hint, since missing annotations are the single most common rejection cause on the review-based surfaces. A privacy policy that’s live by your publish date, and a demo account with sample data a stranger can log into.

Build it once, properly. Every later application then turns into paperwork instead of engineering. If you want the protocol background first, what an MCP server is covers the shape.

One line is worth repeating because it gates more submissions than anything else. Your server has to deliver native value from your own product, not relay requests to someone else’s API. State that you’re first-party, and the pass-through-middleware rejection never comes up.

Decision tree sorting AI surfaces by how they list an MCP server: public submit plus review, self-serve tenant connect, business-development only, and always list in registries first

A quick map of the seven surfaces

With the build done, each surface becomes a short answer rather than a project. Here’s where each one sits, with the deeper walkthrough for every path.

Claude’s Connectors Directory is the clearest public program and the fastest real credibility. Submit, get reviewed, and acceptance is the verified status. Lead here. An app in ChatGPT is the largest consumer reach, but organization verification is a hard gate you clear before you ever submit, and the pass-through rule is strict.

AWS Marketplace and Bedrock AgentCore is the strongest for enterprise procurement and the heaviest lift, because it needs seller registration and a different OAuth grant than most servers ship. Microsoft Copilot’s Agent Store has no MCP-native door at all; you build and maintain a wrapping Copilot agent, then take it through Partner Center.

Mistral’s Le Chat is the rare surface where any admin can connect you in two minutes, while its curated directory stays a business-development conversation. Google Gemini’s three surfaces are the messiest, with no single verified listing and a CLI gallery that’s sunsetting, so the realistic play is the enterprise tenant connection plus a partnership track.

And the seventh isn’t a vendor at all. The MCP Registry and the directories are where you self-publish a record that the rest of the ecosystem reads. Do this one regardless of which vendors you chase, because it’s the feed many clients pull from. For the mechanics of how an agent reaches your tools once any of these connect, how an agent calls your tools walks through it.

What should you do this week?

Don’t try to land all seven at once. Order matters. Sequence them by effort against payoff, and the first few weeks write themselves.

Publish to the registry today, because it’s self-serve and it propagates. Then submit to Anthropic, the clearest public program with the fastest credibility, while you stand up the demo account and privacy policy you’ll reuse everywhere. Do OpenAI’s organization verification next, since it’s the gate that blocks the largest consumer surface. After that, weigh AWS and Microsoft against your enterprise demand, because both are real work and worth it only when customers are asking. Mistral’s custom connector you can switch on whenever a Le Chat user wants it. Google stays a watch-and-connect surface until its picture settles.

We run a server that’s been through these surfaces, and the pattern held every time: the build was the work, and the listing was the easy part once the build was right.

Common questions about listing an MCP server across AI platforms

Which AI platforms actually review and publish an MCP server?
Anthropic (the Claude Connectors Directory) and OpenAI (apps in ChatGPT) run true public submit-review-publish pipelines. Everywhere else you connect inside a customer tenant, negotiate a partnership, or self-publish to a registry, so the process differs by vendor.
Do I need a different server for each platform?
No. A single remote HTTPS server on Streamable HTTP with OAuth 2.0 user consent, annotated tools, a live privacy policy, and a demo account clears most of every program. Build it once and reuse it for every submission.
Where should I list my MCP server first?
Start with the MCP Registry, since it is self-serve and propagates to the discovery directories, then submit to Anthropic for the clearest public review and fastest credibility. Do OpenAI organization verification in parallel because it gates the largest consumer surface.
Is listing an MCP server free?
The registry, the discovery directories, Anthropic, OpenAI, and Mistral custom connectors are free. AWS Marketplace needs seller registration and takes a revenue share on paid listings, and Microsoft requires Partner Center registration, though listings themselves are free.

Forget the vendor for a second. Build one server to the standard every program shares, then publish it to the registry where the ecosystem can find it. Do that, and every surface above turns from a project into a checklist. For why the process behind those tools matters more than the connection itself, why a defined process matters once AI can act makes the case.

About the author

Amit is the CEO of Tallyfy. He has 25+ years of practical experience in technology, entrepreneurship, and operational efficiency. He's been hands-on with AI-first engineering and changing Tallyfy to AI-native workflow automation since Claude Code was first released. He's also an Entrepreneur in Residence at WashU's Skandalaris Center, created the OneDay (Woolf) AI curriculum for their accredited MBA and consults with clients who need help with AI via Blue Sheen. He graduated with a Computer Science degree from the University of Bath. He's originally British and lives in St. Louis, MO.

Find Amit on his website , LinkedIn , or GitHub . Read Amit's bio →

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