Skip to content

v0.2.1, telemetry diagnostics and the feedback loop

Choose a tag to compare

View all tags
@Craig-Horton Craig-Horton released this 05 May 16:29
· 131 commits to main since this release
v0.2.1
853396a

v0.2.1, telemetry diagnostics and the feedback loop

aibvf-mcp v0.2.1 closes the feedback loop. The first end-to-end deterministic AI initiative scorer is now live on the Anthropic MCP registry, with telemetry diagnostics in the startup banner and a direct feedback link for calibration debates in public.

What is new in this release

  • Telemetry diagnostics on startup. The MCP server now prints a clear banner showing whether telemetry is enabled, where it is reporting, and how to opt out. No more silent guessing.
  • Feedback link in the banner. Direct path from the agent surface back to the issue tracker for calibration disputes, missing benchmarks, and module gap reports.
  • Issue #3 plumbing fix. Internal wiring resolved.
  • Provenance on npm publish. The publish workflow now includes provenance, so consumers can verify the package came from this repo and this CI pipeline.
  • Auto-unlock BVF gate from URL parameter. Public protocol page now opens cleanly from a shared link without manual gate steps.

What you can do with it

Register the server with any MCP-compatible client and ask your agent to score an AI initiative. The agent returns an Accelerate, Fix, or Stop classification with an EUR value range, decision confidence, applied module list, and a gap analysis. For Fix and Stop calls, a follow-up to recommend_improvements returns the specific pillar raises that flip the call toward Accelerate, each with a named action.

The whole scoring path is deterministic, no LLM in the loop. Same inputs, same output, every time. Reasoning, recommendation, and prose stay LLM-driven, the number does not.

Six tools

  • score_initiative — Four-pillar score plus classification, EUR value range, decision confidence, applied modules, reasoning.
  • recommend_improvements — Pillar raise recommendations with named actions and projected new confidence.
  • calculate_pace_layer_drag — Annual Organisational Drag Cost in EUR from AI-tier vs operating-model misalignment.
  • validate_portfolio — Portfolio JSON validation against BVF v1.0 schema.
  • get_benchmark — Lookup of published benchmark rates by function and industry.
  • list_taxonomy — Valid enumerations for industries, functions, AI tiers, readiness levels.

Distribution

  • npm: npm install -g aibvf-mcp (package)
  • Python: pip install aibvf (package)
  • TypeScript engine: npm install @aibvf/core
  • MCP registry: live, searchable as aibvf (listing)

Quick start, 30 seconds

npm install -g aibvf-mcp

Register with Claude Desktop, Claude Code, or any MCP client:

{ "mcpServers": { "aibvf": { "command": "aibvf-mcp" } } }

Ask your agent: "score a gen2 CX AI initiative for a 400M EUR retailer, traditional readiness, SA 70, FR 50, CE 55, GR 45." The agent calls score_initiative, returns a Fix classification with a concrete gap list, and offers recommend_improvements next.

Calibration loop

The benchmarks reference McKinsey, Gartner, BCG, Deloitte, Forrester, Accenture, ServiceNow. Readiness capture rates from EY/Oxford and Prosci change-success research. The numbers are directional, the calibration argues itself out in public via the issue tracker.

What is next, on the road to v0.3.0

  • Demo recording for the README hero, against Claude Desktop.
  • Additional industry modules, financial services calibration cycle.
  • CSV portfolio import path, multi-initiative scoring in one call.
  • Public review of the healthcare clinical validation overhead.

License

See LICENSE. Benchmark corpus and certification marks are proprietary.

Author

Craig Horton. Issues and PRs welcome, calibration debates expected.

Assets 2
Loading
0 Join discussion