AI visibility checker: does AI recommend your brand? GEO/AEO, runs locally, BYO keys. CLI + MCP.
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io.github.optifeed/optifeed-radar is an MCP server that AI visibility checker: does AI recommend your brand? GEO/AEO, runs locally, BYO keys. CLI + MCP. Its tool list has not been published yet over stdio, sse and http, requires no API key, and scores 89/100 on MCPpedia's security, maintenance and efficiency rubric.
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"optifeed-radar": {
"env": {
"OPENAI_API_KEY": "sk-..."
},
"args": [
"-y",
"--package=optifeed-radar",
"optifeed-mcp"
],
"command": "npx"
}
}
}Are you the author?
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Open-source AI visibility checker. Now on npm - run it with npx optifeed-radar.
Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.
npx -y 'optifeed-radar' 2>&1 | head -1 && echo "✓ Server started successfully"
After testing, let us know if it worked:
Five weighted categories — click any category to see the underlying evidence.
No known CVEs.
Checked optifeed-radar against OSV.dev.
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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.
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).
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.
The Claude plugin bundles the same skill and starts Radar's MCP server from