Aibvf Mcp
Score and prioritize an AI initiative portfolio with Stop, Fix, or Accelerate calls plus confidence and pace-layer drag signals.
Overview
AI BVF MCP is a MCP server for the Validate phase that scores AI portfolios with Stop, Fix, or Accelerate guidance, decision confidence, and pace-layer drag.
What is this MCP server?
- AI BVF scoring for portfolios with Stop, Fix, and Accelerate outcomes
- Decision confidence metrics alongside pace-layer drag indicators
- stdio npm MCP package (aibvf-mcp) version 0.3.3
- Protocol documentation at bvf-app.vercel.app/protocol
- GitHub source at Bahamas1717/ai-bvf
- MCP server version 0.3.3
- npm identifier aibvf-mcp with stdio transport
- Protocol reference at bvf-app.vercel.app/protocol
What problem does it solve?
You have too many concurrent AI experiments and no shared rubric to kill, repair, or double down without guessing.
Who is it for?
Indie builders or tiny teams running several AI-side projects who want agent-assisted portfolio triage tied to the public BVF protocol.
Skip if: Teams that only need single-feature code review, generic task boards, or scoring unrelated non-AI product lines.
What do I get? / Deliverables
Your agent can apply consistent BVF labels and confidence/drag language so portfolio calls are documented before the next build cycle.
- Stop/Fix/Accelerate labels with confidence and drag framing for each initiative
- Agent-readable portfolio decision narrative for scope meetings
- Repeatable scoring pass you can rerun each sprint or quarter
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Journey fit
Portfolio go/no-go framing belongs on the validate shelf before you scale builds, even though teams revisit scores later in build and operate. Scope decisions—what to stop, fix, or accelerate—map directly to validate → scope prioritization.
How it compares
MCP decision-framework integration, not a generic project-management skill or raw GitHub analytics.
Common Questions / FAQ
Who is AI BVF MCP for?
Solo and indie builders prioritizing multiple AI initiatives who want Stop/Fix/Accelerate scoring inside their agent workflow.
When should I use AI BVF MCP?
Use it during validate-scope reviews and whenever pace-layer drag or low confidence should change what you build next.
How do I add AI BVF MCP to my agent?
Install the npm package aibvf-mcp, add the stdio MCP server entry from the ai-bvf repo protocol docs, and restart your Claude Code or Cursor MCP configuration.