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MCP-C – cloud platform for running MCP agents and apps

Hacker News

MCP-C – cloud platform for running MCP agents and apps

Hello HN! Earlier this year, we shared mcp-agent ( https://github.com/lastmile-ai/mcp-agent ) [1][2], a lightweight framework for building agents with MCP. Since then we have tried to push the protocol to the limits, including hosting agents as long-running tools on MCP [3], and seen other creative approaches surface (mcp-ui, chatgpt apps sdk). Today, we are launching mcp-c – a cloud platform designed for hosting any kind of MCP server, including agents, ChatGPT apps, etc. We are in open beta and free to use, and would love your feedback. Here are some key choices we made: 1) Everything is MCP - Each application is deployed as a remote SSE endpoint, and implements the full MCP spec, including advanced features like elicitation, sampling, notifications and logging. 2) Durable execution via Temporal - We use Temporal as the runtime environment, allowing you to run long-running operations. This is especially critical for agents, which need to pause/resume, be fault-tolerant, and run for a long time. 3) Local to Cloud - we're fans of how easy it is to deploy a Next.js app to Vercel, so we've made it simple to take your local mcp-agent, mcp server, OpenAI app, etc. and deploy it to the cloud. We have several examples and even a handy CLI to get started quickly [4]. It's as simple as: - uvx mcp-agent init - uv init - uv add "mcp-agent[openai]" - uvx mcp-agent login # add your openai key to mcp_agent.secrets.yaml - uvx mcp-agent deploy You can also try out these hosted mcp servers to get an idea. These can be connected to any MCP client (ChatGPT, Claude Desktop/Code, Cursor, etc.) OpenAI Pizza App - https://18t536mliucyeuhkkcnjdavxtyg66pgl.deployments.mcp-age... Basic Agent Server - https://1m82g32x8nkoppinayx0k5ye12oar6vk.deployments.mcp-age... The HN community has been a big part of mcp-agent's growth, and we're super excited to share this with you as well. We would love to hear your thoughts and feedback! --- [1] - https://news.ycombinator.com/item?id=42867050 [2] - https://github.com/lastmile-ai/mcp-agent [3] - https://news.ycombinator.com/item?id=44053754 [4] - https://github.com/lastmile-ai/mcp-agent/tree/main/examples/... [5] - https://docs.mcp-agent.com/get-started/welcome

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, cursor · Missing: mac, macos, model
95%95% 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, including · Missing: supports, reddit linkedin, podcasting
87%87% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, ide, io · Missing: https docs, just released, exist
47%47% 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: apps · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host · Missing: plus, intuitive, reviews
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: growth · Missing: arr, mrr, revenue
25%25% 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.

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