The testing & evaluations platform for MCP servers
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!
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.
On the analytics side, MCPJam competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how MCPJam performed against the three products that launched closest to it on the same day.
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.
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!