Optifeed Radar

Optifeed Radar

Open-source AI visibility checker. Now on npm - run it with npx optifeed-radar.

Is your brand recommended when buyers ask AI? Optifeed Radar checks whether the models behind ChatGPT, Perplexity, Gemini and Claude actually recommend you, and tells you where you stand against competitors. It runs locally, uses your own API keys, and has no Optifeed-hosted backend.

It is built for two kinds of AI agents at once: it measures how AI agents see and recommend you, and it can be run by your own AI agents (CLI, JSON, and an MCP server). People also call this AI visibility, generative engine optimization (GEO), answer engine optimization (AEO), or AI-SEO.

60-second setup

No install needed - npx fetches and runs it. The zero-key audit runs end to end with no API keys and no AI calls:

npx optifeed-radar audit yourbrand.com

It checks AI-crawler access (robots.txt), llms.txt, schema.org structured data, meta basics, and your sitemap, then prints a 0-100 AI-readiness score.

The check pipeline runs once you set at least one engine API key. Put it in a .env file in the directory you run from, or export it:

echo "OPENAI_API_KEY=sk-..." > .env      # any one engine key gets you started
npx optifeed-radar check yourbrand.com

The CLI loads .env from the directory you run it in, so there is no shell setup step. Exporting the keys works too (export OPENAI_API_KEY=...), and an exported key always wins over the same key in .env. config shows which keys were found and which file they came from, never the values.

It discovers your brand, generates a buyer-prompt pack, asks the engines, and scores recommendation, position, and share of voice into one AI Visibility Score. The score reads only the unbranded buyer questions (did the AI surface you unprompted); questions that name your brand are reported separately as reputation. All four engines are verified live against their production APIs (2026-07-20).

Working from a clone instead? Run npx tsx src/cli/index.ts <command> so flags reach the CLI unchanged, or use the npm run dev script with -- before the arguments (npm run dev -- check yourbrand.com --report out.html).

Install the Agent Skill

Radar also ships as an open Agent Skill for Codex, Claude Code, Cursor, and other compatible AI agents. Install it directly from this repository:

npx skills add optifeed/optifeed-radar --skill optifeed-radar

Add -g to make it available across your projects. Then ask, for example:

Use $optifeed-radar to run the free AI-readiness audit on yourbrand.com, explain the three highest-impact findings, and do not start a paid check.

The MCP server supplies executable tools. The Agent Skill supplies the working method around them: start with the zero-key audit, confirm scope and cost before paid engine calls, use a cap, and report sampling limits with the result. The skill can also drive the CLI when MCP is not configured.

Install the Claude Code plugin

The Claude plugin bundles the same skill and starts Radar's MCP server from the published npm package. In Claude Code, run:

/plugin marketplace add https://github.com/optifeed/optifeed-radar.git
/plugin install optifeed-radar@optifeed

Restart Claude Code or run /reload-plugins, then invoke /optifeed-radar:optifeed-radar or ask Claude to audit a domain in plain language. Node 20 or newer is required. The free audit needs no provider keys; paid visibility checks use provider keys from Claude Code's environment.

The standalone skill and Claude plugin do not create a public ChatGPT app. ChatGPT support will be marketed separately after Radar is packaged and tested against OpenAI's plugin and MCP distribution route.

See it in action

Run a full visibility check from the terminal, from brand discovery and buyer prompt generation through live engine queries and scoring.

Watch the 15-second CLI demo

What it does

Optifeed Radar asks real AI engines real buyer questions and measures whether your brand gets recommended - not whether you rank in a search index, but whether the answer an AI gives a buyer names you. Grounded engines (which cite web sources) are reported separately from parametric ones (which answer from model weights alone), because they behave differently. An engine counts as grounded only for the answers where it actually searched: asking for grounded mode is a request a model can decline, so the report says when an engine searched on only some of its answers. METHODOLOGY.md has the formula.

The questions match what you sell. If you make your own products, buyers are asked what to buy and you are measured against rival makers. If you are a shop selling other companies' products, buyers are asked where to buy and you are measured against rival shops - product questions get answered with manufacturers, so scoring a shop on them reports a zero that says nothing about the shop. The tool works this out from your site and stores it as businessType in profile.json; edit it if it guessed wrong.

One level down, shopping does the same thing for individual products you name (beta). Each product gets its own 0-100 visibility score, and the report is ordered by what the engines did: any product they answered about but never recommended leads, since that is the finding worth reading, then the rest by visibility, and last anything the run could not measure at all. The order you list your products in carries no ranking meaning; it only breaks ties between identical scores. Each product is checked twice over - category buying questions that never name it, and questions that do - and when a product is absent the report leads with the rival products the engines named instead, which is the more useful half of a zero. Because every product is asked its own questions, the scores say how decisively each one wins its own shelf, not that one product beats another. You name the products; nothing is imported or crawled.

Use it from your AI agents (MCP)

The optifeed-mcp server exposes the same capability to AI agents. It runs over stdio, and npx fetches it on demand - no clone or build needed.

Claude Desktop (claude_desktop_config.json). The fastest way to open it is Settings -> Developer -> Edit Config, which creates the file if it does not exist yet. On disk it lives at:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "optifeed-radar": {
      "command": "npx",
      "args": ["-y", "--package=optifeed-radar", "optifeed-mcp"],
      "env": {
        "OPENAI_API_KEY": "sk-..."
      }
    }
  }
}

Claude Desktop reads that file at startup, so quit and reopen it after editing.

Claude Code (.mcp.json in your project):

{
  "mcpServers": {
    "optifeed-radar": {
      "command": "npx",
      "args": ["-y", "--package=optifeed-radar", "optifeed-mcp"]
    }
  }
}

Cursor (.cursor/mcp.json) and Windsurf (mcp_config.json) use the same shape:

{
  "mcpServers": {
    "optifeed-radar": {
      "command": "npx",
      "args": ["-y", "--package=optifeed-radar", "optifeed-mcp"]
    }
  }
}

Working from a clone instead? Build first (npm install && npm run build), then run the server with node pointed at the built entrypoint - replace /path/to/optifeed-radar with your clone path:

{
  "mcpServers": {
    "optifeed-radar": {
      "command": "node",
      "args": ["/path/to/optifeed-radar/dist/mcp/index.js"]
    }
  }
}

Example prompts

Once it is connected, ask your AI agent in plain language. These map onto the five tools and the arguments they accept: