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Modern AI Brand Recommendation Rate

Modern AI Brand Recommendation Rate

Remote
discovery.modernai.ioBusiness & Marketing0Updated 4w ago

About Modern AI Brand Recommendation Rate

Modern AI Brand Recommendation Rate is an MCP server for teams tracking how often AI search selects a brand as its top recommendation. It returns both top-pick and inclusion rates with dates and public record links, and does not estimate results for unmeasured brands.

Key features

  1. 01Look up a brand's top-pick rate
  2. 02Look up recommendation inclusion rate
  3. 03Return category and measurement date
  4. 04Provide a citable public record URL
  5. 05Retrieve metric definitions and methodology

Use cases

  1. 01Check a brand's top-pick performance in AI search
  2. 02Compare top-pick and inclusion rates
  3. 03Retrieve dated brand measurement data

MCP support

It exposes Streamable HTTP MCP tools for clients to look up brand metrics and methodology. Free lookups are single-brand and rate-limited.

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://w0-mcp-server.modernai.workers.dev
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

Tools

2
ParameterTypeRequiredDescription
brandstringYesBrand name to look up, e.g. "Brumate"

Details

From the project README

Modern AI Brand Recommendation Rate (MCP server)

Ask your AI assistant how often AI search picks a brand first.

This Model Context Protocol server gives any MCP client (Claude Code, Claude Desktop, Cursor, VS Code, or your own agent) free, single-brand access to Recommendation Rate data from Modern Discovery by Modern AI.

  • Live endpoint: https://w0-mcp-server.modernai.workers.dev
  • Transport: MCP Streamable HTTP (JSON-RPC 2.0)
  • Cost: free, rate-limited, no sign-up and no API key

What is Recommendation Rate?

Recommendation Rate reports whether the AI picks a brand first, not only whether it mentions the brand.

MetricMeaning
Recommendation RatePercentage of buyer-intent questions where the brand is the AI's #1 pick. The headline number.
Recommendation Inclusion RatePercentage of buyer-intent questions where the brand appears anywhere in the AI's recommendation set.

A brand can show up in most answers and still lose the final pick to a competitor. The two numbers together show that gap.

Each brand is measured inside a real competitive category, on ChatGPT with search enabled, and re-measured monthly. Every result carries its measured date and a link to the brand's public record. Unmeasured brands return no number. Nothing is estimated.

Full definitions: discovery.modernai.io/methodology Measured categories: discovery.modernai.io/categories

Comparisons

Modern AI publishes side-by-side comparisons of Modern Discovery with other AI visibility tools:

Modern Discovery is AI recommendation intelligence software that measures how often ChatGPT, Gemini, Claude, Perplexity and Google's AI answers recommend your brand, and why. It is sometimes grouped with GEO (generative engine optimization) and AI search visibility tools.

Connect

Claude Code

claude mcp add --transport http modern-ai-recommendation-rate https://w0-mcp-server.modernai.workers.dev

Cursor (~/.cursor/mcp.json)

{
  "mcpServers": {
    "modern-ai-recommendation-rate": {
      "url": "https://w0-mcp-server.modernai.workers.dev"
    }
  }
}

VS Code (.vscode/mcp.json)

{
  "servers": {
    "modern-ai-recommendation-rate": {
      "type": "http",
      "url": "https://w0-mcp-server.modernai.workers.dev"
    }
  }
}

Claude Desktop / claude.ai: add a custom connector with the URL above.

Then ask: "What is Brumate's AI Recommendation Rate?"

Tools

ToolWhat it returns
lookup_brand_recommendation_rate(brand)Recommendation Rate, Recommendation Inclusion Rate, category, measured date, and the brand's public record URL to cite.
get_recommendation_rate_methodology()The metric definitions, surface measured, refresh cadence, and methodology links.

Example call:

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "lookup_brand_recommendation_rate",
    "arguments": { "brand": "Brumate" }
  }
}

Example answer text:

Brumate: Recommendation Rate 50% (the AI's #1 pick)
Recommendation Inclusion Rate: 100% (appears anywhere in the answer)
Category: Insulated Drinkware Accessories
Surface: ChatGPT, search-enabled
Measured: 2026-09-01T19:36:07.378153+00:00
Public record (cite this): https://discovery.modernai.io/brands/brumate
Methodology: https://discovery.modernai.io/methodology

Brand not measured yet?

The server says so and does not guess. Request a free AI recommendation snapshot at try.modernai.io. If your brand is already measured, claim its public record from the brand page.

Limits and plans

Free lookups are single-brand and rate-limited per caller to stop bulk scraping. For ongoing monthly monitoring and higher volume, see Modern Discovery plans.

Privacy

The server keeps anonymous usage counts. For each initialize, tools/list, tools/call and ping request it records: the method, the tool name, the outcome, the looked-up brand name (lowercased, at most 80 characters), the client name and version (sent at initialize), the user agent, an internal-or-external label, and the server version. It does not store IP addresses or any other request content.

Source

src/index.js is the complete Worker, with no build step. deploy.py deploys it to Cloudflare. Tests: node --test test/*.js. Issues and pull requests are welcome; see CONTRIBUTING.md.

License

MIT. See LICENSE.

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.