bhived-mcp
Eliminates context loss and skill duplication across AI agent workflows by providing persistent, shared memory and tool discovery—reducing setup time and ena…
bhived is an MCP server that gives AI agents shared memory, skills, and tool discovery. install once, works in Claude Code, Cursor, and 15+ other agents.
- Ask Claude to save project decisions and reuse them across multiple coding sessions.
- Generate a shared skill library that multiple AI agents can access for common tasks.
- Find and load previously configured tools without reinstalling for each new project.
Eliminates context loss and skill duplication across AI agent workflows by providing persistent, shared memory and tool discovery—reducing setup time and enabling agents to coordinate effectively across multiple platforms.
Engineering teams running multi-agent workflows in Claude, Cursor, or third-party tools seeking unified tool discovery and state persistence.
https://github.com/ArtKeyAi/bhived-mcp
By ArtKeyAi
How to Get It
claude mcp add bhived-mcp -- npx -y bhived-mcp
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
Once it’s connected, paste this into Claude:
Save project decisions and reuse them across multiple coding sessions
Trust Signals Auto-scanned
Data & Access
Community Pulse New
No community discussions found yet. This doesn't mean the tool isn't good — it may be new or serve a niche use case.
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 35 GitHub stars; 1 contributors; last commit 11d ago; license Apache-2.0.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
- Single maintainer. Consider the risk if this person stops maintaining the project.
How to evaluate tools before deploying →
Data shown here comes from public APIs and automated scanning. Reviewer notes reflect one person's experience. This is not a security certification or legal recommendation. Always evaluate tools according to your own organization's policies.
Evaluation
Scored from trust signals (evidence-eval-v1): 35 GitHub stars; 1 contributors; last commit 11d ago; license Apache-2.0.