Your agent's integration fix passes CI. The data is still wrong. FetchSandbox MCP reproduces the real failure on your code, fixes it, and proves the fix held. A receipt, not a vibe. 70+ API sandboxes. One config block in Cursor or Claude Code.
Writing the integration stopped being the hard part. Checking that it actually works is the whole job now, and that's the half your agent can't do.
The gap
Your agent can write a Stripe integration. It can't run one. It writes the code, tells you it's done, and you find out in production whether that was true. FetchSandbox gives the agent already in your editor two things it doesn't have: somewhere real to run integration code, and a way to prove the fix worked.
Why the proof check matters
A customer paid for 5 seats. A retry gave them 10, then 15. An agent fixed it, and after the fix nobody got any seats at all. Tests still passed because the duplicates were gone. Almost any fix makes the error disappear. Far fewer make the data right.
So the gate asserts the exact end state a correct implementation leaves, and refuses to go green when it can't reproduce the bug first.
Setup
One block in your MCP config. No API key, no signup. Works in Claude Code, Cursor, Cline, Windsurf, and Codex. 70+ ready-made sandboxes: Stripe, HubSpot, Clerk, Resend, Twilio and more, free to try.
We hit #3 on our first launch. The ask afterward was exactly this: don't just give me a sandbox, tell me my fix actually worked. This is that.
Has your agent ever confidently fixed something that was still broken?
The 5 seats turning into 15 is the version of this that actually costs money. We hit the same shape at Zeplik, output that parses fine and passes every assertion we wrote, wrong in a way only the end state shows. Green CI on a bad write is worse than a red build because nobody goes back and looks. I'd want to know if the receipt catches partial failures too, 3 of 5 writes landing and the rest silently dropped.
FetchSandbox addresses a real weakness in AI-generated integrations: verifying actual end states rather than merely checking for successful responses. The stateful sandboxes, failure injection, and proof receipts make this especially valuable for testing billing and authentication workflows before deployment.
One optional improvement would be showing a side-by-side state diff in each receipt, highlighting the expected and actual values across the full workflow. That would make subtle failures, such as a plausible but incorrect response body, easier to detect during review. Congrats on the launch!
Testing and verifying AI integration fixes in a sandbox before deploying saves so much headache. Congrats on shipping!
"almost any fix makes the error disappear, far fewer make the data right" is the whole thing, and it is the same shape as the problem we keep running into.
to answer your question: yes, and the worst one was not even an agent. we added an anthropic key and three things were wrong at once. opus rejects an explicit temperature outright, one haiku model id had been retired and returned 404, and our own code sent a temperature on every call. nothing failed in testing because nothing in testing actually called it. any customer who had selected opus would have had every single reply fail on the first try. we were offering an integration nobody had ever executed.
the related one scares me more. we ran eight models against a live pricing api and two of them read the wrong row of a price ladder that was sitting in their context. one quoted 39.00 for an order that costs 9.60, the other quoted 9.00. the 39.00 gets caught by anyone glancing at it. the 9.00 does not, and that is the one that reaches a customer.
so the thing i would want to know about the proof step: does it assert the response shape, or the actual values? a 200 with a plausible wrong body is the failure that survives every check we have tried.
Curious how this compares to traditional contract testing tools like Pact when driven by an LLM agent? Seems much more accessible for rapid iterations.
Testing webhook idempotency with AI agents is an absolute nightmare they always silently fail or fake the fix. Forcing the agent to prove it worked with an actual receipt URL before merging is brilliant. qq Are you planning to let us add custom internal enterprise APIs to the sandbox list soon? Upvoted...
The end-state assertion is the strongest part here. In SaaS billing and automation work, preventing a duplicate event is only half the problem—the final subscription, entitlement, and audit state all need to agree. Does the sandbox also let teams test reordered events and delayed retries across the same workflow?
About FetchSandbox MCP on Product Hunt
“The MCP that proves your AI's integration fixes work”
FetchSandbox MCP launched on Product Hunt on August 23rd, 2026 and earned 191 upvotes and 30 comments, earning #2 Product of the Day. Your agent's integration fix passes CI. The data is still wrong. FetchSandbox MCP reproduces the real failure on your code, fixes it, and proves the fix held. A receipt, not a vibe. 70+ API sandboxes. One config block in Cursor or Claude Code.
FetchSandbox MCP was featured in API (98.6k followers), Developer Tools (518k followers) and Artificial Intelligence (476.6k followers) on Product Hunt. Together, these topics include over 207.4k products, making this a competitive space to launch in.
Who hunted FetchSandbox MCP?
FetchSandbox MCP was hunted by Raj Nagulapalle. 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 FetchSandbox MCP stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Raj Nagulapalle
Raj here, one of the co-founders.
Writing the integration stopped being the hard part. Checking that it actually works is the whole job now, and that's the half your agent can't do.
The gap
Your agent can write a Stripe integration. It can't run one. It writes the code, tells you it's done, and you find out in production whether that was true. FetchSandbox gives the agent already in your editor two things it doesn't have: somewhere real to run integration code, and a way to prove the fix worked.
Why the proof check matters
A customer paid for 5 seats. A retry gave them 10, then 15. An agent fixed it, and after the fix nobody got any seats at all. Tests still passed because the duplicates were gone. Almost any fix makes the error disappear. Far fewer make the data right.
So the gate asserts the exact end state a correct implementation leaves, and refuses to go green when it can't reproduce the bug first.
Setup
One block in your MCP config. No API key, no signup. Works in Claude Code, Cursor, Cline, Windsurf, and Codex. 70+ ready-made sandboxes: Stripe, HubSpot, Clerk, Resend, Twilio and more, free to try.
We hit #3 on our first launch. The ask afterward was exactly this: don't just give me a sandbox, tell me my fix actually worked. This is that.
Has your agent ever confidently fixed something that was still broken?