This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Pulse shows usage, errors, and latency for your MCP tools without collecting prompts, arguments, or results. Use the open-source SDK locally or connect to Pulse Cloud for a shared dashboard. Try the public demo without signing up.
Once an MCP server is running, the next questions are practical: which tools are getting used, which calls fail, and where are handlers slowing down? Pulse brings those signals into one dashboard without collecting prompts, tool arguments, or results.
You can explore tool usage, handler outcomes, p50/p95 duration, and release comparisons. The SDK is MIT licensed and can run locally; Pulse Cloud is an optional managed destination with a shared dashboard. The Cloud interface supports English and Turkish.
Try the interactive demo without signing up: https://pulse.reviseflow.io/en/demo. Its sample data is clearly labelled. The public SDK and setup guides are linked on this launch.
We built Pulse with GPT-6 Astra in Codex throughout the project: the independent MIT SDK, managed Cloud service, dashboard, tests, and documentation. The challenge was turning an idea into a working product with clear privacy boundaries and trustworthy measurements. Thanks to ChatGPT and OpenAI for the tools that helped us build it.
If you build MCP servers, which would help you most: finding slow tools, investigating failures, or comparing releases? I'd love your feedback on the workflow and the signals you need.
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About Pulse on Product Hunt
“See which MCP tools get used, fail, and slow down”
Pulse was submitted on Product Hunt and earned 1 upvotes and 1 comments, placing #156 on the daily leaderboard. Pulse shows usage, errors, and latency for your MCP tools without collecting prompts, arguments, or results. Use the open-source SDK locally or connect to Pulse Cloud for a shared dashboard. Try the public demo without signing up.
Pulse was featured in Analytics (173.8k followers), Developer Tools (519.8k followers), Artificial Intelligence (479.1k followers), GitHub (41.4k followers) and OpenAI Day (39 followers) on Product Hunt. Together, these topics include over 254.9k products, making this a competitive space to launch in.
Who hunted Pulse?
Pulse was hunted by Selim Enes. 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 Pulse stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Selim Enes
Hey Product Hunt! I'm Selim, the maker of Pulse 👋
Once an MCP server is running, the next questions are practical: which tools are getting used, which calls fail, and where are handlers slowing down? Pulse brings those signals into one dashboard without collecting prompts, tool arguments, or results.
You can explore tool usage, handler outcomes, p50/p95 duration, and release comparisons. The SDK is MIT licensed and can run locally; Pulse Cloud is an optional managed destination with a shared dashboard. The Cloud interface supports English and Turkish.
Try the interactive demo without signing up: https://pulse.reviseflow.io/en/demo. Its sample data is clearly labelled. The public SDK and setup guides are linked on this launch.
We built Pulse with GPT-6 Astra in Codex throughout the project: the independent MIT SDK, managed Cloud service, dashboard, tests, and documentation. The challenge was turning an idea into a working product with clear privacy boundaries and trustworthy measurements. Thanks to ChatGPT and OpenAI for the tools that helped us build it.
If you build MCP servers, which would help you most: finding slow tools, investigating failures, or comparing releases? I'd love your feedback on the workflow and the signals you need.