AI agents can lose track of previous work, repeat tasks, or struggle to share context across workflows. Common Context MCP is an open-source Model Context Protocol server exploring a shared context layer for AI agents. ✨ Key capabilities: • Shared context between AI agents • Work checkpointing and progress recovery • Recording failures and roadblocks • File claiming and task coordination Built for developers experimenting with AI agents, MCP-compatible tools, and multi-agent workflows.

Hey Product Hunt! 👋

I'm the maker behind Common Context MCP, an open-source project exploring how AI agents can share context and coordinate their work.

I started building this because I'm interested in a practical challenge in multi-agent systems: how can agents preserve progress, record roadblocks, and avoid duplicating work?

Common Context MCP explores this through shared context, work checkpointing, failure recording, and file-claiming mechanisms.

I'm building this in the open and would genuinely appreciate feedback from developers working with MCP servers and AI agents.

I'd love to know:

• How do you manage shared context between AI agents?

• What problems do you face when coordinating multiple agents?

• What would make this project more useful in your workflow?

Thanks for checking it out! 🚀