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Magnitude MCP – vision-first browser interaction for Claude Code

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Magnitude MCP – vision-first browser interaction for Claude Code

Hey HN - Anders and Tom here. We made a browser MCP using the same vision-first scaffolding as our SOTA (94% WebVoyager) browser agent Magnitude. This approach is more flexible and robust than DOM-based interactions and avoids all the associated edge cases. It works particularly well with Claude Code, since Claude is already trained for computer-use tasks and can interact with browser elements precisely using vision alone. It's 100% open source. The setup is very simple if you want to try it: ``` npm i -g magnitude-mcp@latest claude mcp add magnitude -- npx magnitude-mcp ``` Then spin up Claude Code and ask it to do something in the browser. The MCP allows Claude Code (or any coding agent) to: - Open a browser with a persistent profile - Click, type, drag, etc. using pixel-based coordinates - Automatically stay aware of current state with a screenshot after each interaction - Coordinate multiple actions at once for efficiency We made this MCP because we realized it would be really helpful in our own engineering workloads. We are building a massive number of first-party integrations for Sagekit (our workflow automation platform), and to do so we need to give Claude Code as many tools as possible to act with a high degree of autonomy, including a browser it can use reliably. The only downside is that only certain models can use the MCP effectively, because not all models can pinpoint exact pixel coordinates based on a screenshot. These models roughly include Claude (Sonnet 3.7, Sonnet 4, Opus 4), Qwen 2.5 VL-based models (Qwen 2.5 VL 72B, UI TARS, etc.), and a few others specifically trained for it. Given that we personally use Claude for all our coding anyway, this seemed acceptable. After using Claude Code to build 15+ integrations in a week, it became obvious that certain tools are necessary to take us out of the dev loop more often and produce high quality code autonomously. A browser was an obvious place to start, and we already had a browser agent we could repurpose. So far, this is what we've personally found it useful for: - Seeing and interacting with web apps as it builds features or fixes issues - Configuring dummy data that can't be accessed programmatically - Browsing documentation on sites where fetch doesn't work - Improvised frontend testing Anyway, we thought we would share in case other engineers find it useful in their workloads. It can also be used in non-engineering work (like booking a flight!!) if you so desire.

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, claude, model · Missing: mac, agents, macos
99%99% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, apps, way · Missing: mobile apps, ios, entrepreneurs
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: open source, ide, io · Missing: https docs, excited, just released
38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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