Arka – an MCP gateway that makes MCP adoption possible

Hacker News

We built Arka because MCP adoption is slow in real teams. The same three problems show up everywhere. 1. Context gets too big Teams add more MCP servers and the context grows fast. The model sees too many tools and picks the wrong one. Accuracy drops and workflows break. This blocks adoption because the setup works in demos but fails in real use. 2. Setup does not scale Each MCP server needs its own config, port, and auth. Teams end up running many servers with no shared control. It is hard to track and easy to break. Most teams stop here because it takes too much time to maintain. 3. No enterprise security No SSO. No audit logs. No user rules. Most teams pass around raw tokens. Security teams block the rollout. This stops MCP adoption in companies that care about compliance. Arka solves these adoption problems with one gateway. One setup for all MCP servers • One token per user with SSO • User rules and tool rules • Tool filtering to keep context small • Better accuracy with many tools • Logs for every call • Open source or cloud We are also adding: • Agent access with safe rules • Workflows you can convert into your own MCP server Our goal is simple: make MCP adoption possible at scale. Repo: https://github.com/KenisLabs/arka-mcp-gateway Cloud: https://www.kenislabs.com Would love your feedback!

Visit product View on Hacker NewsNov 20, 2025

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

89%89% 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 · Missing: supports, reddit linkedin, podcasting

60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

47%47% 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, io · Missing: https docs, excited, just released

39%39% 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

20%20% 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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