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ToolStorm
Test the failures that happen after a tool succeeds
Reproduce tool failures and check the side effects that a successful response can hide. A lost response can turn a retry into a duplicate shipment. The browser lab compares scripted policies, not LLM performance. No real shipments are made.
A shipping tool creates the shipment, then loses its acknowledgement. The caller retries and receives a valid confirmation. A response-based success check passes, but there are now two shipments.
I built ToolStorm to test this boundary. It injects faults before or after Python tool execution, records committed effects and checks explicit contracts. Recorded calls can be replayed offline without invoking the live tool again.
The browser lab runs the same Python source through Pyodide. Try the lost-acknowledgement scenario and compare no retries, unchecked retries and validated retries. These are scripted policies and fictional shipments, not measurements of LLM performance.
The library is MIT licensed with no runtime dependencies. The lab needs no account, model key or payment. Your application still owns the retry and idempotency logic.
I would appreciate examples of recovery bugs that a normal mocked exception does not capture. What should the next failure recipe be?
Try it: https://toolstorm.web.app
Source: https://github.com/shi1720/tools...
Built by Shivam Gupta. More work and contact: https://shivamgupta.web.app/
LinkedIn: https://www.linkedin.com/in/shiv...
About ToolStorm on Product Hunt
“Test the failures that happen after a tool succeeds”
ToolStorm was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #54 on the daily leaderboard. Reproduce tool failures and check the side effects that a successful response can hide. A lost response can turn a retry into a duplicate shipment. The browser lab compares scripted policies, not LLM performance. No real shipments are made.
On the analytics side, ToolStorm competes within Developer Tools and GitHub — topics that collectively have 561.8k followers on Product Hunt. The dashboard above tracks how ToolStorm performed against the three products that launched closest to it on the same day.
Who hunted ToolStorm?
ToolStorm was hunted by Shivam Gupta. 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.
For a complete overview of ToolStorm including community comment highlights and product details, visit the product overview.
Shivam Gupta