We build agent products and MCP servers are becoming a real part of that surface area, so a dedicated testing/eval layer is exactly the kind of tool that should have existed already. Being able to run evals and CI/CD gates against a server before shipping it, instead of manually poking at it in Claude or ChatGPT and hoping nothing regressed, is the big win here. The multi-client angle (testing across ChatGPT, Claude, Copilot) is smart since MCP behavior isn't always consistent client to client.
MCPJam
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!
Clueso
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.
MCPJam
@prajwal_prakash Yeah definitely man! You can run a prompt side-by-side across a bunch of major AI clients and see the full trace and the exact input and output tokens at each step in our playground, then do the same in evals test cases. Check it out!
Clueso
@prathmesh_patel Awesome, will that give a dollar value too? That'd be superb
MCPJam
Stoked for this one!!
MCPJam
@marcelo_jimenez we're jamming!
Crucial tool for anyone building in and fir AI. Have my vote!
MCPJam
@viggos Awesome to hear, thanks Viggo!
Great product!!!, definitely the best platform for mcp development and testing!
MCPJam
@olartgabo let's go make MCP effective!
Congrats on your launch, Prathmesh! The concept sounds really good, I'll definitely take a look.
MCPJam
@ab80 awesome to here @ab80 - let us know how it goes!
It doesn't get better than MCPJam if you want to test your MCP server!!!
MCPJam
@ignaciojimenezr let's do it!