Blog·Engineering

The UX and technicalities of awesome MCPs

By Dhravya Shah·7 min read

Supermemory blog banner reading "The UX And Tech of Memory MCP" with a rising row of blue 3D cubes

Last month, we launched the Supermemory MCP, mostly to test our own infrastructure and get some initial traction. It blew up.

To my absolute surprise, the initial launch itself got half a million impressions (!!!). Then, we launched and got #2 on ProductHunt too. Seems like people absolutely loved the concept and it was a massively successful side-project. In this blog, we'll talk about the reasons why this MCP did really well - and learnings along the way.

What did we do differently?

If you look at any popular MCP server right now, the ultimate realizations are:

  • MCPs are only used by developers right now.
  • Authentication always seems to get in the way
  • It's a terrible pain to install MCPs today.
  • MCP clients always want to behave differently.
  • Long-standing connections (SSE) keeps breaking.
  • There's a huge usecase problem.

To get through each of these hurdles, we chose a set of user experience and technical decisions, to ensure that the MCP is really newbie proof (from both a user perspective and not-well-done MCP clients)

1) Choose SSE instead of STDIO

At the time of building the MCP, most servers were using STDIO. But, the reality is, my mom could never ever use something that needs a terminal command to run.
I realized that there will be more and more MCP clients that would add SSE support, especially on Web-based clients like Claude.ai. In April there were barely any famous web-based clients (none that I can remember), so this was a huge bet.

My previous MCP servers like apple-mcp, which also got popularity, were completely local-only. This felt like a good bet to make because MCP SSE servers can be made to work over STDIO as well.

2) No auth at all.

Next step was authentication: There's a lot of fuss in the "Auth with MCP" space, and in my opinion, none of them offer a good user experience. The main issue in my opinion is, again, the variability of how the clients work.

So, we decided to nuke auth completely - and generate a unique URL for every user that comes to the website. In fact, we made it so easy that all that the user has to do is mcp.supermemory.ai, and the ONLY thing that they see on the screen is a huge URL to copy.

From a technical perspective, we use react-router loader to:

  1. Check if userId is in session
  2. If it is, query the database for that userId
  3. Else, generate a random ID
  4. Commit it to the session
export async function loader({ request, context }: Route.LoaderArgs) {
    const cookies = request.headers.get("Cookie")
    const session = await getSession(cookies)

    if (session.has("userId")) {
        const userId = session.get("userId")!
        // ... get user memories
        return data({
            message: "Welcome back!",
            userId,
            memories,
        })
    }

    session.set("userId", nanoid())
    const userId = session.get("userId")!

    return data(
        ...
        {
            headers: {
                "Set-Cookie": await commitSession(session, {
                    expires: new Date("9999-12-31"),
                }),
            },
        },
    )
}

There are obviously some huge drawbacks to this approach:

  • If the user clears the cookies, they will lose their memories
  • If anyone finds out the URL for the user, they might be able to access it.

These drawbacks were fine for us, because the user would "persist" their URL by Copy+pasting it to an MCP client anyways. Which means they would always store it somewhere.
And the url path parameter acted as an API key.


This also means that the MCP server itself would have to handle the userId on a dynamic url.
But we'll get there.

Flowchart: if a userId is in cookies, query the Supermemory API for memories; if not, generate a random ID and commit the session
How our authentication works

This did make things a little challenging - like how do you handle different client connections again on different domains?
Most MCP server libraries are built for one URL only- so we "wrapped" our functions into a createSupermemory "meta mcp" so, technically, a dynamic server is generated for every single unique user.

        server.get("/sse", async (c) => {
            const userId = c.get("userId")

            return streamSSE(c, async (stream) => {
                this.transport?.connectWithStream(stream)

                await bridge({
                    mcp: muppet(createSuperMemory(userId, c.env), {
                        name: "Supermemory MCP",
                        version: "1.0.0",
                    }),
                    transport: c.env.transport,
                    // logger: logger,
                })
            })
        })

This does not really have any performance implications, but just better UX because the URL will look like mcp.supermemory.ai/userid/sse and /userid/messages

Beautiful, as things should be.

3) Made an installation CLI to make it dead simple.

Over a quick afternoon, I vibecoded this quick tool to install MCPs with a single CLI command.

This meant that I could provide a CLI command for whichever MCP client had a local-only config.
Interestingly, a lot of cool people contributed to the CLI like Namanya, Kent C. Dodds, and more, to add more clients, and make the installation UX even better!

Supermemory MCP install UI with client tabs (claude, cursor, cline, windsurf) and an npx install-mcp command

Users no longer had to "Edit JSON Config" files to install a simple MCP server.
It was always Copy+paste. Either the URL to direct SSE connections, or the CLI command.

Simply beautiful. As I like it.

Terminal showing "Successfully installed MCP server" for supermemory-mcp with the claude client

This is what I mean by good UX. You need to go out of your way to give a good user experience. Software is flaky and broken, so the easier you make it for people to not fuck up, the better. Supermemory does all this because we care. Even for our non-revenue making projects :)

4) Provide prompts.

The supermemory MCP also provides a prompt built-in for clients that really, really don't want to listen.
For example, when we launched, the MCP didn't work on Claude because it really wanted to use a resource instead of a tool. Which meant that we had to write an unhinged prompt 😆

addToSupermemory tool toggle with a description stressing it must be called as a tool, not a resource

Anyways, by prompts, I mean actual MCP protocol "prompts". So that users can actually click on "use Supermemory Prompt" whenever they explicitly need to use memory

Claude chat composer with a Supermemory Prompt.txt attachment and the Sonnet 4 model selected

This is not perfect, but all we could do to make MCP clients unfuck themselves :)

5) Host your MCP servers on Cloudflare.

SSE is a weird protocol. You have to have an extremely long-running connection for the messages to actually work. Memory absolutely requires this because clients may send one memory in 30 minutes, or 6 hours, you never know.

Fortunately, the smart folks over at Cloudflare have been working on a way to run "Durable" objects / connections for years now. And MCP happens to be the perfect use case for something like this. Initially when we launched the MCP, users constantly complained about "Connection lost" and stuff like that.
I was also lucky to work on this tech, during my time at Cloudflare before starting this company. So, I trust the Agents SDK and genuinely think that there's no better platform to put MCPs on.

Worse still, most platforms charge for Execution time (total time running compute, including waits) - not CPU time (only time actually using CPU), but for MCPs you almost always have to just "wait" for messages to do anything with it.
Again, fortunately Cloudflare only charges for CPU time which means I can have unimaginably long connections, and that definitely happened.

Two dashboards contrasting CPU time peaking near 5.67ms P99 against request duration spiking to ~1.54M ms P99
While the actual CPU time was a few milliseconds only (mostly MCP transport stuff), the total connection times were MILLIONS of milliseconds each.

Bonkers 😅

6) Use-cases.

On a more non-technical side, I believe that whenever you sell something, lead with examples. All the demos of the supermemory MCP were things that I personally use it for, every day. In fact, my personal URL has 400+ memories about me already - projects I'm building, my preferences, my girlfriend's name, and a lot more. 😃
I made & posted videos of me actually using the product, and whenever something interesting happens, I would post it on Twitter as well!

The result: growth.

It turns out, more MCP clients started supporting direct SSE connections - include Claude, x.com/DhravyaShah/status/19304649496772… - And instantly after Claude launched integrations, people started looking for interesting integrations to add.
Supermemory was one of them, on day 1.

Today, it might be the most used memory MCP - but I can't tell for sure 😅
But all this could not be possible without all the background work...

The background work

We were able to make & ship supermemory MCP in ~5 hours of actual work time. Just look at the repository - it's the simplest thing you'll see in your life. It's a react router app that makes fetch calls to the Supermemory API.

The MCP was made as a "Customer" of the base API, that we have been working for months on. If you want to read more about that, we wrote a blog about it too!

But the TL;DR is that we've been obsessing over memory for a while now, topping almost every benchmark and objectively the best way to build chat apps / agents with memory today

Bar chart: Supermemory beats the next best memory provider on P@1, Recall@10, NDCG@10, and MAP@10 in the LOCOMO benchmark
Supermemory vs next best provider, in the LOCOMO benchmark.

There's a lot to build, and we're just getting started. We'll continue obsessing over user and developer experience, and continue on our vision of building the best memory engine on the planet.

Star the MCP repository, start using supermemory and leave your thoughts on Twitter (tag @supermemoryai)

  1. An update to supermemoryWe've discontinued the supermemory company brain and Nova. Everyone who was charged has been refunded, our MCP and plugins continue to run, and we're going all in on the memory engine.
  2. SMFS: making agentic retrieval 55% cheaper AND more accurateWe launched SMFS.ai (Supermemory Filesystem) a few weeks ago, with a simple bet: We can redesign the filesystem specifically for agents, with special files, structures, and commands that it can use for it's tasks. Today, SMFS is used by hundreds of companies to power their agents.
  3. Introducing Dynamic Dreaming: supermemory now connects the dots, for you.Dreaming is magical. TLDR: We're launching Dynamic Dreaming in supermemory today, which automatically works if you're using supermemory in any way - API, OpenClaw, Hermes agent, etc.
  4. Dear reader, we just made supermemory insanely cheap... the Context CloudWhen I first started building supermemory, I had one goal: To build the best memory system for AI. I would talk to customers, and find out that memory was not the only thing they needed - They were all setting up 7-8 different vendors at the same time.
  5. Introducing @supermemory/tools v2.0.0Today we're releasing v2.0.0. This release unifies the API across all agents sdk integrations from AI SDK to Mastra, makes conversation identity a first-class concept, and ships with memory saving on by default.
  6. supermemory will make your Hermes-agent crazy powerfulToday, we are launching supermemory support to your Hermes agent TLDR: you can use supermemory now in your Hermes agent, it totally free to get started - https://supermemory.ai/docs/integrations/hermes In case you missed it: Hermes Agent is a self-improving AI agent from Nous Research.
  7. Solving the Precision-Recall Tradeoff: Search Result AggregationWhen you're building memory for AI, search is your foundational layer. The way search generally works is straightforward: the user defines a query, and then sets a limit (top-K) on how many search results they want returned. Usually, this is set to 10 or 20.
  8. Infinitely running stateful coding agentsWe built a plugin for Claude Code and OpenCode that gives your coding agent persistent memory. It remembers your preferences, learns your codebase, and never loses context mid-conversation. The result is an agent you can run for months without starting over.
  9. Why everyone is complaining about OpenClaw's memory (it sucks) - and why supermemory fixes it.TLDR: Today, we are releasing a new version of our openclaw plugin - https://github.com/supermemoryai/openclaw-supermemory. This post is going to be a bit technical, so bear with me (or bookmark for later!) In this post, I will talk about what we do about OpenClaw memory, and how we fix it.
  10. We added supermemory to Claude Code. It's INSANELY powerful now...Today, we are launching the Supermemory plugin for Claude Code! TLDR: You can use supermemory in claude code now. - https://github.com/supermemoryai/claude-supermemory Claude code has genuinely changed how I work. But there's this one thing that drives me crazy...
  11. Clawd / Molt bot's memory SUCKS. We gave it supermemory.I'm the founder of supermemory. Clawd/Molt bot is blowing up right now, with many, many use cases. I set it up, too, and have been using it through telegram. TLDR: just go to https://supermemory.ai/docs/integrations/clawdbot to set up supermemory for your clawd bot.
  12. Catch up with our UNFORGETTABLE Launch WeekOver the last year, one belief has guided almost everything we’ve built at Supermemory AI becomes meaningfully useful only when it remembers. Memory shouldn’t be something developers rebuild from scratch. It shouldn’t be fragile, expensive, or trapped inside a single tool.
  13. Empowering the Next Generation of Founders: Supermemory Startup ProgramIf there’s one thing we’ve learned while building Supermemory, it’s that most startups don’t fail because they didn't build features; they fail when infrastructure slows them down, or they built too slow.
  14. Building code-chunk: AST Aware Code ChunkingAt Supermemory, we're building context engineering infrastructure for AI. A huge part of that is dealing with code: ingesting repos, understanding structure, and making it searchable. The problem is that most code chunking solutions are terrible. We built code-chunk to fix this.
  15. Supermemory raises $3 million with the best memory engine for LLMsToday, I am excited to announce our first funding round to accelerate our mission of building an interoperable, scalable and reliable memory for LLMs and agents. Memory is one of the hardest challenges in AI right now.
  16. Unified Memory That Works Where You Work: Your Second Brain With SupermemoryHi everyone, I’m Dhravya, the founder of Supermemory. I want to start with a little story behind why this product means so much to me. You can also skip straight to what it is and how it works below.
  17. Supermemory just got faster on PlanetScaleWhat is Supermemory? Supermemory completes the missing part of the LLM puzzle: memory. Just as memory is crucial for human intelligence, it's essential for truly intelligent AI systems.
  18. Faster, smarter, reliable infinite chat: Supermemory IS context engineering.People are obsessed with prompts and prompt engineering. Sure, what you say is important, but what the model knows when you say it is the difference between a stateless text generator and an intelligent AI system. In short, context is the most crucial component.
  19. We solved AI API interoperabilityOne API to rule them all, One spec to find them, One library to bring them all and in the TypeScript, bind them. When we were building the the Infinite Chat API, initially, we only supported the OpenAI format. This was fine, until a lot of our customers started asking, asking for more.
  20. Architecting a memory engine inspired by the human brainLanguage is at the heart of intelligence, but what truly powers meaningful interaction is memory — the ability to accumulate, recall, and contextualize information over time. Large Language Models (LLMs) have mastered language, but memory remains their Achilles’ heel.