
FetchSandbox MCP



















Last launch ended with 91 comments. I answered every one, and in most of them I said "not yet, that's next."
Six weeks on, here's what actually happened.
Done: @maschiojv asked about Paddle, it now has 21 curated failure patterns instead of a bare spec import.
I audited an AI-written billing service this week. 22 findings, 10 critical.
None of them crashed. All returned 200. Four that stuck with me: an auth check that silently lets anyone in when one env var is missing, SQL injection in the one query that wasn't parameterized, a broken permission boundary so anyone can attach a charge to a workspace that isn't theirs, and a Stripe retry that double-grants seats because there's no idempotency check anywhere.
Since our last launch we've been heads-down on one thing: making "this integration works" actually mean something. Here's what changed, and why we're more confident than a month ago.
What we shipped?
Five integrations now recognize the bugs people actually hit and prove the fix with a real before/after:
Most API mocking tools stop at verifying basic status codes, which hides major async flaws. FetchSandbox addresses the actual root causes of integration failures by exposing code to messy production conditions like webhook retries, duplicate events, and rate limits. The native MCP integration is an excellent architectural choice. It allows AI coding agents in tools like Cursor or Claude Code to immediately run validation loops, catch edge case bugs, and verify non-idempotent handlers right inside the IDE without wasting real API quotas.
The preconfigured coverage for mainstream APIs is solid, but the platform needs a more streamlined workflow for importing custom OpenAPI specs to test private internal microservices. The automated repair loop works well when an agent applies a fix, but providing more verbose telemetry visualization for state transitions during deep nested retry cycles would simplify manual debugging. Additionally, expanding the local CLI capabilities to allow offline container execution would benefit teams with strict data security requirements.
I evaluated traditional API testing frameworks like Postman alongside standard custom mock servers built with MSW or local tools. While Postman is great for manual endpoint testing, it requires heavy configuration to simulate asynchronous webhook lifecycle failures. Custom local mocks become brittle and time consuming to maintain for dozens of separate APIs. I chose FetchSandbox because it packages realistic behavior profiles for over 60 major services into a single zero configuration environment that integrates directly into agentic development workflows.
The agentic integration engine addresses a critical void in traditional API mocking by simulating deep state lifecycle events, network failures, and complex async workflows instead of basic static responses. Operating via a native Model Context Protocol (MCP) server layer, FetchSandbox injects directly into AI development systems like Cursor, Claude Code, and Windsurf to replicate real-world environment anomalies like out-of-order webhook delivery, token timeouts, and late event retries. By maintaining a persistent, runnable record of caught bugs and integration breaks inside the development framework, it empowers both developers and coding agents to programmatically verify and fix failure boundaries over 60+ pre-seeded cloud APIs without draining real-world testing quotas.
The dependency on curated, pre-seeded API schemas limits immediate test coverage when engineering highly customized, proprietary in-house endpoints or niche third-party services not yet included in the platform's core global library. Because the workflow requires explicit verification runs where a faulty handler and a fixed router must observably diverge, automating the ingestion of arbitrary, unmapped integration failures still demands manual curation rather than happening entirely via autonomous background capture. Additionally, running high-throughput concurrent agent simulations across complex, multi-layered webhook loops can introduce isolated tracking overhead within localized IDE terminals lacking decentralized telemetry views.
I’ve constructed isolated local mock environments using traditional tools like WireMock, Postman, or custom stub libraries, and utilized generalized integration hubs like Merge. While standard mocking platforms adequately return simple 200 OK statuses, they fail to replicate stateful race conditions, lack native IDE context plugins for autonomous agents, and demand extensive manual script maintenance to mirror actual cloud breaking changes. I chose FetchSandbox because it provides a dedicated, re-runnable proof sandbox that programmatically exposes architectural edge cases before code hits staging branches.
thanks man!!!, genuinely thoughtful review, and the WireMock/Postman/Merge comparison nails why this exists, mocks hand back a 200 and cant touch stateful race conditions.
honestly your needs improvement section is right, so let me just agree in the open:
1/custom/proprietary specs, correct. you can import any openapi spec and the basic calls run, but the deep curated failure library (the real dup/late/stale reproductions) only exists for the seeded set today. widening that, and getting to generic proof for any imported spec, is the work ahead.
2/autonomous capture, also correct. today its curated and proof-gated, you point it at a known failure mode and it reproduces + proves. learning arbitrary failures from your own runs in the background is the direction, not shipped, and i wont pretend it is.
3/one you were a little generous on, ill call it myself, deterministic out-of-order replay isnt live yet. it falls out of timing today, not a pinned deliver-this-before-that sequence. its literally the next thing im building.
4/and the concurrency/observability point is fair, the terminal view gets thin under heavy parallel runs, better telemetry there is on the list.
appreciate you taking it seriously enough to find the real edges. thats worth more to me than the stars.
connecting real testing scenarios (webhooks, retries, async state) via MCP straight into Cursor/Claude Code instead of a separate dashboard is the part that actually saves time day to day
docs on the 60+ API library could use more real failure-case examples instead of just the happy path per API, since that's the whole point of the tool
thanks man.., and yeah thats exactly the bet, the value shows up when its right there in cursor/claude while youre already arguing with the agent, not in another dashboard you have to go open.
and your improvement point is a fair hit, honestly a little embarrassing, a tool whose whole point is failure cases showing mostly happy-path examples in the docs is backwards. leading each api's docs with the real failure cases (the duplicate webhook, the retry on stale state, the out-of-order) is exactly what it should do. thats going near the top of the list. good catch.
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?
Install, takes about thirty seconds.
Add this to your MCP config. That's `.mcp.json` in your project root for Claude Code, or Settings → MCP for Cursor:
```json
{
"mcpServers": {
"fetchsandbox": {
"command": "npx",
"args": ["-y", "fetchsandbox-mcp@latest"]
}
}
}
```
No API key, no signup. Claude Desktop needs a full quit and reopen, Cmd+Q, not just closing the window.
Try it without touching your own code first
Type this to your agent, not your terminal — the ./fetchsandbox prefix is just how you tell it to use the MCP:
"./fetchsandbox Test my Stripe webhook for duplicate deliveries"
Takes about a minute and you'll get a receipt URL.
Or the one I'd show a skeptic:
"./fetchsandbox Paddle events arrived out of order and reactivated a paused subscription"
Then point it at something real
Name the API and what you're seeing:
"./fetchsandbox Stripe webhook signature verification fails for valid events"
@rnagulapalle giving the coding agent an actual sandbox environment to fail against before calling a PR ready is a massive unlock. Congrats on the launch🙌
@priya_kushwaha1 yeah "fail against before calling a PR ready" is exactly the frame, the agent needs a place to be wrong safely before it's confident. appreciate you getting it so quickly!
@charan_t_m thanks, really appreciate that! the proof-check piece is the one that surprised me most too, turns out "it's fixed" and "here's a receipt showing the failure flipped" land very differently when you're reviewing an agent's PR.
@rnagulapalle are you using deterministic sequence pinning for the out of order webhook replays yet? saw in the thread it was next up, getting that right without flake is really hard.
@mohsinproduct not yet, still pinning the sequencing logic down. you're right that flake is the real enemy there, replaying out-of-order is easy, replaying it deterministically so your test doesn't pass Tuesday and fail Thursday is the whole problem. working through the state machine ordering before I ship it so it's actually trustworthy. what's your current setup for testing that edge, curious if you've hit a specific pattern that's hard to reproduce.
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...
@vikramp7470 Thanks so much, the silent fake fix is exactly what kept me up at night building this. Custom internal APIs work today actually, drop any OpenAPI 3.x spec at `/import-spec` and it spins up a fully stateful, schema-validated sandbox in about 5 seconds, so you're not limited to the built-in specs. Would love to hear what internal APIs you're working with if you give it a shot.
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?
@modiyilreji yes, exactly the problem i was trying to solve. delayed retries are covered via scenario switching mid-workflow, you can flip the sandbox into a degraded or rate-limited state between steps and re-trigger. reordered events are supported too; workflows in the spec configs let you sequence the same events in different orders so you can assert on the final state, not just whether each event "succeeded." the end-state check is what actually matters in billing flows.
"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.
@jernej_jan_kocica three failures at once and none of them visible until the key hit prod, that's exactly the shape that's hard to catch in any test that doesn't actually run the provider's validation. the retired model id returning 404 especially, that's the kind of thing you only find when something real is on the other end. glad this resonated, and that war story is going straight into how i explain the "why" of this thing.
@adamkamaneh 70+ you can run right now, Stripe, Paddle, HubSpot, Clerk, Resend, Twilio, GitHub, Notion, Shopify, Discord, Datadog, RevenueCat. Payments, auth, email, messaging, CRM are the clusters with the most depth.
All of them run, real state, webhooks, lifecycle. About 15 also have the failure library on top, the curated set of known ways that specific API actually breaks in prod. Duplicate delivery, retries on stale state, signature verification failing on valid events. Those are the ones where it reproduces a named bug instead of just exercising the happy path. Stripe is deepest.
HubSpot is the one I've pushed hardest on the CRM side.
If yours isn't listed, point it at any OpenAPI spec and it stands up a sandbox from that. You just don't get the failure library for it, you'd be exercising the API, not the known failure modes.
Where this is going since you asked about kinds: single third-party APIs are the starting point. The version I actually care about is standing up a whole internal service graph, several services and the third parties they depend on, all at once. Built a version of that this week for a company that rebuilds systems it acquires. Three services in one scenario, caught the new implementation skipping a call the old one made while everything else looked fine. That's the thing.
Which one were you thinking of? Genuinely useful to know what people reach for first.
@adamkamaneh 67 today — Stripe, GitHub, Twilio, OpenAI, Clerk, WorkOS, Resend and more. Anything not in the catalog, import an OpenAPI spec and you get a stateful sandbox with the same webhooks and failure injection.
What are you integrating with?
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!
@adityaharish2002 that side-by-side diff idea is exactly right, right now the receipt shows the final state but subtle field-level drift across a multi-step workflow is easy to miss. adding a before/after diff per step is on the roadmap and your framing of it (expected vs actual across the full workflow, not just the last call) is actually the cleaner way to think about it. appreciate the specific callout.
@tehreem_fatima5 pact is great for locking down the contract between a known consumer and provider in CI. fetchsandbox is less about "did the schema match" and more about running the full lifecycle: stateful CRUD, webhook delivery, auth failures, rate limits, all from inside the agent's IDE session. for an LLM agent the rapid iteration piece matters a lot, it can reproduce a specific failure, fix the code, and get a proof receipt back rather than just validating shape.





FetchSandbox
thanks man, and you framed the core exactly, most tools stop at the 200 and hide the async stuff. the Postman/MSW comparison is right too, custom mocks rot fast across dozens of apis.
your improvement points are fair, and let me be straight on one of them:
1/the "automated repair loop where an agent applies a fix", i have to correct this one, its not actually shipped. today fetchsandbox reproduces the failure and proves whether a fix holds, but it does not apply the fix for you. the agent-fixes-then-reproves loop is the next thing im building, not live. didnt want you crediting something thats still roadmap.
2/streamlined custom openapi import for private internal microservices, agreed. you can import a spec today, but making that path smooth (and getting the deep failure library to work on your own endpoints, not just the seeded set) is real work ahead.
3/clearer state-transition visualization during deep nested retry cycles, fair. the trace is there but the observability for nested retries could be a lot better. on the list.
4/offline / self-hosted container execution for strict-data teams, noted, and its come up before. the engine runs server-side today, an offline mode for security-sensitive teams is a real enterprise ask im tracking.
appreciate the depth, finding the actual edges is worth more to me than the rating.