Burnish, a UI for any MCP server (no LLM, no chat)

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Burnish is a UI for MCP servers. You point it at a stdio or SSE endpoint and it renders each tool as an interactive form from the tool's JSON schema, then maps the response to a card, table, chart, or pipeline view based on the shape of the JSON. No LLM involved. The point is to see what a server actually does without wiring up a chat client or writing an inspection script. MCP Apps (the official ext-apps extension) lets servers ship their own custom UI, but only if the server author builds one. Most don't. Burnish works with any server, including ones that have never heard of it, because it reads the schema and infers components from the output shape. The hosted demo above runs a showcase server with 34 tools. Locally: `npx burnish -- npx -y @burnishdev/example-server`. Source is AGPL-3.0: https://github.com/danfking/burnish . The thing I genuinely want to know is whether a generic explorer like this solves a real problem, or whether chat clients and MCP Apps already cover it well enough. Feedback welcome.

Visit product View on Hacker NewsApr 22, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: mcp, apps · Missing: mac, agents, macos

71%71% 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

51%51% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: pipe, io, including · Missing: https docs, excited, just released

28%28% 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 · Strong signals: active · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

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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