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!
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.
@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!
Report
Maker
It doesn't get better than MCPJam if you want to test your MCP server!!!
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.
@marcophp glad you like Swarms! At Asana I was struggling to generate the "right" prompts to throw into our evals when production o11y gave us so little user intent and we resorted to best practiec checklists. Being able to spin up agents to act like my users with clear goals and just see how they prompt and perform across clients is fun to find those patterns for sure
Replies
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!
MCPJam
Stoked for this one!!
MCPJam
@marcelo_jimenez we're jamming!
Great product!!!, definitely the best platform for mcp development and testing!
MCPJam
@olartgabo let's go make MCP effective!
Crucial tool for anyone building in and fir AI. Have my vote!
MCPJam
@viggos Awesome to hear, thanks Viggo!
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!
It doesn't get better than MCPJam if you want to test your MCP server!!!
MCPJam
@ignaciojimenezr let's do it!
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.
MCPJam
@marcophp glad you like Swarms! At Asana I was struggling to generate the "right" prompts to throw into our evals when production o11y gave us so little user intent and we resorted to best practiec checklists. Being able to spin up agents to act like my users with clear goals and just see how they prompt and perform across clients is fun to find those patterns for sure