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Pantheon MCP – a central server for AI agent definitions

Hacker News

Pantheon MCP – a central server for AI agent definitions

Hi all, since I began building with agentic systems, I kept copying and sharing Markdown-based agent definitions across projects and devices. Syncing was a pain. So I built Pantheon MCP, a server that always provides the latest versions of an agent definition from a collection of currently 42 agents. Now I have to only add the MCP server to a new project and everyone on the project gets the same agent definitions all the time. In addition, using it allows the system to choose dynamically the correct agent based on the task at hand. I’ve been running it internally — now I’m releasing it publicly. Feedback welcome. I hope others find it useful as well. Happy experimenting! V.D. https://pantheon-mcp.com https://github.com/valado/pantheon-mcp

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, agentic · Missing: mac, macos, cursor
92%92% 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.
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.
AppSumoMay struggle as an AppSumo deal · 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: ide, io · Missing: https docs, excited, just released
32%32% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
22%22% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
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.

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