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Pulse
See which MCP tools get used, fail, and slow down
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.
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.
On the analytics side, Pulse competes within Analytics, Developer Tools, Artificial Intelligence, GitHub and OpenAI Day — topics that collectively have 1.2M followers on Product Hunt. The dashboard above tracks how Pulse performed against the three products that launched closest to it on the same day.
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.
For a complete overview of Pulse including community comment highlights and product details, visit the product overview.
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.