AI Recommendation Readiness Audit MCP MCP server

This MCP server provides a read-only, deterministic assessment of a business's readiness for AI-driven recommendations. It evaluates factors such as business size, data maturity, and technical stack to generate a readiness score, tier classification, gap analysis, and prioritized implementation roadmap. The service is designed to help businesses understand their position in the AI-mediated economy without performing any consequential actions or guaranteeing specific rankings.

Endpoint and access

No sign-in: your client connects and lists tools immediately, with no account or key.

No sign-inEndpoint checked 2026-10-05Operated by The Black Friday Agency

MCP endpoint (Streamable HTTP)

https://canairecommendmybusiness.com/api/mcp

Vendor documentation

What you can do with it

Assess business readiness for AI recommendations
Generate gap analysis for AI adoption
Obtain prioritized implementation roadmap
Evaluate data maturity and technical stack

Tools advertised by the last live check:

assess_ai_recommendation_readiness
get_ai_readiness_framework

Connect from your client

Each snippet registers the same endpoint; pick the client you use.

This server is operated by the named third party. Listing and endpoint checks are not an endorsement or a security audit. Review the operator's permissions, terms, and data handling before connecting. How listings are checked and corrected.

Claude Code

Run this once in a terminal. It registers a Streamable HTTP server. Use --scope project to write .mcp.json for the repo.

Claude Code configuration

claude mcp add --transport http ai-recommendation-readiness-audit-mcp https://canairecommendmybusiness.com/api/mcp

Codex

Run this once. Codex stores MCP servers in ~/.codex/config.toml (or .codex/config.toml in a trusted project).

Codex configuration

codex mcp add ai-recommendation-readiness-audit-mcp --url https://canairecommendmybusiness.com/api/mcp

Cursor

Merge this server into .cursor/mcp.json in your project, then enable ai-recommendation-readiness-audit-mcp in your MCP settings.

Cursor configuration

{
  "mcpServers": {
    "ai-recommendation-readiness-audit-mcp": {
      "url": "https://canairecommendmybusiness.com/api/mcp"
    }
  }
}

Cline

Merge this into cline_mcp_settings.json (Cline → MCP Servers → Configure). The type must be streamableHttp for a remote HTTP server.

Cline configuration

{
  "mcpServers": {
    "ai-recommendation-readiness-audit-mcp": {
      "type": "streamableHttp",
      "url": "https://canairecommendmybusiness.com/api/mcp",
      "disabled": false
    }
  }
}

VS Code

Merge this server into .vscode/mcp.json. Use MCP: List Servers to start ai-recommendation-readiness-audit-mcp and inspect its tools.

VS Code configuration

{
  "servers": {
    "ai-recommendation-readiness-audit-mcp": {
      "type": "http",
      "url": "https://canairecommendmybusiness.com/api/mcp"
    }
  }
}

Gemini CLI

Run this once. It writes .gemini/settings.json in the current project; pass --scope user for ~/.gemini/settings.json.

Gemini CLI configuration

gemini mcp add --transport http ai-recommendation-readiness-audit-mcp https://canairecommendmybusiness.com/api/mcp

Claude Desktop

Merge this into claude_desktop_config.json (macOS: ~/Library/Application Support/Claude/, Windows: %APPDATA%\Claude\), then restart Claude Desktop.

Claude Desktop configuration

{
  "mcpServers": {
    "ai-recommendation-readiness-audit-mcp": {
      "url": "https://canairecommendmybusiness.com/api/mcp"
    }
  }
}

Windsurf

Merge this into ~/.codeium/windsurf/mcp_config.json, then refresh MCP in Cascade. Windsurf remote servers use serverUrl, not url.

Windsurf configuration

{
  "mcpServers": {
    "ai-recommendation-readiness-audit-mcp": {
      "serverUrl": "https://canairecommendmybusiness.com/api/mcp"
    }
  }
}

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