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ContextVM – Running MCP over Nostr

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ContextVM – Running MCP over Nostr

Hi HN, I'm Gzuuus, the creator of ContextVM this is my first post here, hope you find it interesting! I started building ContextVM some months ago, this is an open protocol that runs the Model Context Protocol (MCP) over Nostr. In practice, it’s a transport for MCP that lets you expose remote servers without needing a domain, inbound ports, or OAuth, clients, and servers only need an outbound internet connection. The problem I ran into: When deploying a remote MCP server you can feel the pain. You usually need a domain name, a static IP, TLS certificates, port forwarding, and some way to auth (often OAuth). That friction makes it hard to spin up a service on your laptop or a Raspberry Pi and expose it safely. ContextVM solves this at the transport layer. It replaces HTTP/SSE with Nostr relays that act like a distributed, stateless message bus: Identity/auth: public keys instead of OAuth. Network/transport: clients and servers only need outbound connection to relays, so you can run behind NAT or a strict firewall. Security: client↔server traffic is end-to-end encrypted; relays just route encrypted events blindly. Addressing/discovery: servers (and clients) are addressable by public key, no domains required. Servers can be announced on relays instead of central registries. Private servers can skip announcements entirely, clients connect if they know the server’s public key. Payments: Recently we included a payment method agnostic specification defining the lifecycle of payments, this is CEP-8 https://docs.contextvm.org/spec/ceps/cep-8/ The best part, you don’t need to rewrite your existing MCP servers: - `cvmi`: a CLI to expose any local HTTP/Stdio MCP server over ContextVM. For example, `npx cvmi serve -- npx -y @modelcontextprotocol/server-filesystem /tmp` - `ctxcn`: inspired by `shadcn/ui`. It connects to a server, reads its MCP schema, and generates a type-safe TypeScript client into your codebase. - TypeScript SDK: for those who want to build natively on the protocol. - Project site: It works as a tool for discovery, and debugging, you can run your server locally and connect through the site We'd love feedback, questions, or critiques. If you want to dig deeper, you can visit the project page for a blog, read the docs, or just build something. It's all open source, permissionless, and fun! We are also publishing a bi-weekly newsletter on substack: https://contextvm.substack.com/ Code: https://github.com/contextvm Project site: https://contextvm.org Docs: https://docs.contextvm.org

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, mcp, new · Missing: mac, agents, macos
88%88% 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: started · Missing: supports, reddit linkedin, podcasting
85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, open source, existing · Missing: https docs, excited, just released
67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: month, way · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% 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.

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