Run User Testing, Swarms, Evals, and CI/CD gates on your MCP server to see if users actually succeed in ChatGPT, Claude, and Copilot. Test local servers via desktop app, CLI, or SDK.
Users now start in ChatGPT, Claude, Cursor, and other AI clients. They reach your product through your MCP server.
That means your users often aren’t in your product anymore. You can’t see what they prompted for, how the agent interpreted it, or whether your server helped them get the result they wanted.
I saw this firsthand leading MCP technical strategy at Asana, including our ChatGPT and Claude launches. We were building high-stakes enterprise integrations, but we had no reliable way to test them the way we test normal software- or to know whether they worked once they reached real users.
I started using MCPJam for those problems after re-connecting with my former coworker who created the project. brought it to more of our developers, and worked it into our CI/CD pipeline. I joined the team because I kept hearing the same issue from other companies building for agents.
So, what does “good” look like for MCP? For us, it means users reliably get the outcome they came for, across the AI clients they use.
That’s what we’ve been building toward. MCPJam now helps you test the full workflow, from the first prompt to the expected result:
* Swarms: Simulate users with different goals and prompts to find where workflows break across AI clients. * User Testing: Watch how real users interact with your MCP product, where they get stuck, and how they feel about the results. * Evals: Turn those workflows into repeatable tests that check whether users get the expected outcome. * CI/CD: Run those evals across AI clients before each release to catch regressions.
MCPJam has grown from a debugging tool into a continuous testing and evaluation workflow for MCP servers.
If you’re building an MCP server or agent-facing product, give MCPJam a try. What is the hardest thing for you to test? We love hearing about your MCP server builds!
Looks great! Particularly excited for evals that can run for different clients. I’m often dealing with customers using gemini, chatgpt, claude etc asking why their prompts aren’t doing what they expect when communicating with our MCP server, and evals will help bridge that gap
Cool stuff! Our MCP usage has been growing a lot, could definitely use this.
I use MCPJam daily and it's my goto for testing MCP Servers, MCP Apps and teaching others how to do it.
Congratulations @prathmesh_patel and the MCPJam team! The Descope team can speak from first-hand experience how helpful MCPJam has been as a debugging tool while we worked to support Cross-App Access (XAA) in our platform.
Question for you: what role do you see MCP (and by extension MCPJam) playing in agentic commerce over the coming years?
Congrats on this launch! This is my go-to MCP inspector! configuring the capabilities like the actual clients saves soooo much time when testing!
The amount of times ChatGPT/Codex/Claude have changed how they deal with plugins/MCP's etc. is insane (every few days it keeps changing), but it's been impressive how MCP Jam is able to stay on top of the latest changes and let us know how our MCP will interact in these different apps/scenarios.
Because we've definitely tried to stay on top of it without it, and it's been a mess.
Do you have ways of also helping test/evaluate different access levels to tools? In case we want to create actors that access our MCP in cloud agents that have a restricted set of scopes/permissions?
Congrats on the launch team!
Love, love, love. Most people aren't thinking broadly enough about MCPs. They are products in and of themselves at this point and need to be treated as such.
Congrats!! Testing MCP servers across different clients is a nightmare. Having automated evaluations in CI/CD is amazing!! :)
Is there a way to automate evaluations instead of manually testing each tool?
What kind of tests do you recommend running before pushing an MCP server into production?
Honestly, MCPJam has become one of the best and most useful dev tools for MCPs. Having all these features in one place makes the whole workflow way smoother!!
Congrats on your launch,Prathmesh! The concept sounds really good, I'll definitely take a look.
Congrats on the launch, Prathmesh! As a solo developer building hyper-lightweight web apps from scratch (pure PHP and vanilla JS), I am obsessed with reducing friction and tracking exactly how features behave. The concept of 'Swarms' to simulate ifferent user goals is brilliant. When building standalone tools, testing user flow behavior without heavy framework overhead is a massive challenge. Reducing the abstract evaluation of AI-native interactions into repeatable ests is definitely a game-changer for independent makers.
This is super cool! We've actually struggled a bit with answering questions like 'How many tokens will this workflow consume?' when we're discussing our MCP with procurement teams. Is this something you can help with? Would be awesome to get to see a given prompt/workflow and the net cost with the MCP across clients, and benchmark those.
Great product!!!, definitely the best platform for mcp development and testing!
About MCPJam on Product Hunt
“The testing & evaluations platform for MCP servers”
MCPJam launched on Product Hunt on September 17th, 2026 and earned 154 upvotes and 43 comments, placing #7 on the daily leaderboard. Run User Testing, Swarms, Evals, and CI/CD gates on your MCP server to see if users actually succeed in ChatGPT, Claude, and Copilot. Test local servers via desktop app, CLI, or SDK.
MCPJam was featured in Open Source (68.8k followers), Developer Tools (519.6k followers), Artificial Intelligence (478.9k followers) and GitHub (41.4k followers) on Product Hunt. Together, these topics include over 251.2k products, making this a competitive space to launch in.
Who hunted MCPJam?
MCPJam was hunted by Prathmesh Patel. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how MCPJam stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Prathmesh Patel
Hey Product Hunt 👋 Prathmesh, CEO of MCPJam here.
Users now start in ChatGPT, Claude, Cursor, and other AI clients. They reach your product through your MCP server.
That means your users often aren’t in your product anymore. You can’t see what they prompted for, how the agent interpreted it, or whether your server helped them get the result they wanted.
I saw this firsthand leading MCP technical strategy at Asana, including our ChatGPT and Claude launches. We were building high-stakes enterprise integrations, but we had no reliable way to test them the way we test normal software- or to know whether they worked once they reached real users.
I started using MCPJam for those problems after re-connecting with my former coworker who created the project. brought it to more of our developers, and worked it into our CI/CD pipeline. I joined the team because I kept hearing the same issue from other companies building for agents.
So, what does “good” look like for MCP? For us, it means users reliably get the outcome they came for, across the AI clients they use.
That’s what we’ve been building toward. MCPJam now helps you test the full workflow, from the first prompt to the expected result:
* Swarms: Simulate users with different goals and prompts to find where workflows break across AI clients.
* User Testing: Watch how real users interact with your MCP product, where they get stuck, and how they feel about the results.
* Evals: Turn those workflows into repeatable tests that check whether users get the expected outcome.
* CI/CD: Run those evals across AI clients before each release to catch regressions.
MCPJam has grown from a debugging tool into a continuous testing and evaluation workflow for MCP servers.
If you’re building an MCP server or agent-facing product, give MCPJam a try. What is the hardest thing for you to test? We love hearing about your MCP server builds!