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NimbleTools – A K8s runtime for securely scaling MCP servers

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NimbleTools – A K8s runtime for securely scaling MCP servers

We originally built this because we needed a way to run MCP servers inside private clouds and on-prem. Most of the teams we work with can’t just punch a hole through the firewall to hit some external service. They need agents that can securely connect to databases, internal APIs, and legacy systems — all inside their own infrastructure. Agentic systems like LangChain and LangGraph are powerful, but they need reliable tool access without a human in the loop. MCP is the right protocol for that, but actually deploying MCP servers was painful. Every one had different requirements (stdio vs HTTP), and scaling them in production was messy. So we built NimbleTools Core: * Team-Ready from Day One: multi-workspace support, RBAC, private registries. * Universal Deployment: run stdio servers and HTTP servers with the same interface. * Horizontal Scaling: MCP servers scale up/down automatically with demand. * Community Registry: browse and deploy pre-configured servers, or publish your own. * Kubernetes-Native: CRDs + operator pattern for lifecycle management. Quick start (literally one command gets you running locally): ``` curl -sSL https://raw.githubusercontent.com/NimbleBrainInc/nimbletools... | bash ``` We’ve been using this for more complex customer deployments already, but wanted to give back by open-sourcing the core engine. It’s still early — today NimbleTools Core gives you a solid runtime for deploying MCP servers on Kubernetes. Looking ahead, we’re experimenting with features outside the current MCP spec that we think will matter in production, like: - Session management: keep context across multiple tool calls, not just one-off requests - Smarter auto-scaling: more granular policies beyond just horizontal replicas - Tool discovery & selection tools: helping agents automatically find, choose, and route to the right MCP server at runtime We’d love feedback from the community on which direction matters most. https://github.com/NimbleBrainInc/nimbletools-core We just opened up a discord too ( https://discord.gg/znqHh9akzj ). Bit of a ghost town right now, but hoping to change that!

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, agentic · Missing: mac, macos, cursor
91%91% 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
72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: host, interface, calls · Missing: plus, platform, intuitive
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: way · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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