AI & Machine Learning

aibvf.com

Provides tools to assess, score, and improve AI initiatives, including cost calculation for operational drag.

1 endpoint13 known toolsFirst detected July 25, 2026Last detected July 29, 2026

ENDPOINT 1

https://mcp.aibvf.com/api/mcp

No auth detected

MCP server metadata

Name: io.github.Craig-Horton/aibvf-mcp
Version: 0.14.10

Capabilities

tools

Known tools 13

assess_ai_initiative

The front door for one AI investment decision.

Inferred read-only

score_initiative

Canonical-field scorer for one AI initiative.

Inferred read-only

score_portfolio

Score several AI initiatives as one AI BVF v1.

Inferred read-only

recommend_improvements

Turn a Fix or Stop verdict into the change plan that could earn a re-score, with pillar targets, named plays, owners, stop conditions, cost of waiting and a deadline.

Inferred read-only

calculate_pace_layer_drag

Quantify the annual EUR cost of an AI ambition outrunning the operating model: queues, hand-offs and slow decisions that prevent the organisation capturing the value already assumed in the case.

Inferred read-only

validate_portfolio

Check whether a supplied AI BVF v1.

Inferred read-only

get_benchmark

Look up the disclosed AI BVF planning rates behind the value model for one business function and industry.

Inferred read-only

list_taxonomy

Return the exact industry, function, AI-tier and readiness values every AI BVF calculation accepts.

Inferred read-only

diagnose_process

Diagnose a single existing business process from operational evidence and return the intervention, modelled net EUR saving, efficiency gain, verdict and confidence.

Inferred read-only

infer_readiness

Measure organisational readiness from process data, so the investment case does not depend on an untested maturity claim.

Inferred read-only

sequence_portfolio

Turn a scored AI portfolio into three waves with gates over a configurable horizon, so the roadmap respects the change capacity of each business function.

Inferred read-only

map_to_taxonomy

Map everyday business language to the canonical AI BVF values required by the scoring tools.

Inferred read-only

assemble_portfolio

Assemble a valid AI BVF v1.

Inferred read-only

CONNECT WITH APPROVAL

Client installation

Review this server and its permissions before adding it. Secret placeholders must be set locally.

Codex

~/.codex/config.toml

[mcp_servers.io-github-craig-horton-aibvf-mcp]
url = "https://mcp.aibvf.com/api/mcp"
enabled = true

Claude Code

.mcp.json

{
  "mcpServers": {
    "io-github-craig-horton-aibvf-mcp": {
      "type": "http",
      "url": "https://mcp.aibvf.com/api/mcp"
    }
  }
}

Claude Desktop

Settings → Connectors → Add custom connector

Name: io-github-craig-horton-aibvf-mcp
Remote MCP URL: https://mcp.aibvf.com/api/mcp

Add this remote URL as a custom connector in Claude Desktop. Availability depends on the user plan and workspace policy.

Cursor

.cursor/mcp.json

{
  "mcpServers": {
    "io-github-craig-horton-aibvf-mcp": {
      "url": "https://mcp.aibvf.com/api/mcp"
    }
  }
}

Visual Studio Code

.vscode/mcp.json

Add to Visual Studio Code

{
  "servers": {
    "io-github-craig-horton-aibvf-mcp": {
      "type": "http",
      "url": "https://mcp.aibvf.com/api/mcp"
    }
  }
}

Generic MCP

Client-specific MCP configuration

{
  "name": "io-github-craig-horton-aibvf-mcp",
  "transport": "streamable-http",
  "url": "https://mcp.aibvf.com/api/mcp"
}

MCP Inspector

Run the official MCP Inspector locally and enter the indexed Streamable HTTP endpoint.

TRUST AND VERIFICATION EVIDENCE

Trust Data Available

BuiltWith Trust API v2 evidence for aibvf.com was fetched 2026-07-26T08:57:38.139Z and is being refreshed.

Trust status Neutral

aibvf.com is assessed as Neutral: No suspicious signals found, but no strong positive signal either

Indexed

Evidence is source-attributed and does not guarantee that a third-party server is safe. Risk labels are conservative metadata heuristics.