LLM Optimizer — Claude Code MCP server: brand visibility analytics
Scores how AI models and AI search engines mention your brand, then hands back a visibility number and fixes. Useful when SEO alone no longer covers discovery.
claude mcp add --transport http llmopt https://llmopt.metavert.io/mcpWhat LLM Optimizer does
LLM Optimizer runs as a remote MCP server at https://llmopt.metavert.io/mcp, so your agent talks to it over HTTP. Ask it about a site, a YouTube channel, Reddit threads or search presence and it returns an AI Visibility Score from 0 to 100. The composite weights content optimization at 30%, video at 20%, Reddit at 20%, search at 15% and model testing at 15%.
Six analysis types sit behind that score. Answer Engine Optimization grades pages on quotation density, statistics, source citations and machine readability. Video goes through two passes: a fast model reads transcripts and captions, then a reasoning model scores four pillars. Reddit data comes from public .json endpoints with a Cloudflare WARP proxy as fallback. Search visibility checks robots.txt rules for AI crawlers, structured data and content freshness. LLM Knowledge Testing sends your target queries straight to Anthropic, OpenAI, Gemini and Grok and compares the answers.
Nothing installs locally, but the knowledge test spends calls against those providers. Without working access on the service side, that dimension comes back empty while the rest still runs on public data.
What it helps with
- see whether models mention your brand at all
- find pages AI search engines rarely cite
- judge YouTube transcripts as a signal for models
- read Reddit sentiment around your product
- compare visibility against a competitor on the same queries
Example prompts for the agent
- Analyze example.com visibility in AI search and list the weak pages
- Pull a Reddit report on how people talk about our brand
- Compare how Anthropic and OpenAI answer queries about our product
How to install LLM Optimizer
Platform CLI: remote server
claude mcp add --transport http llmopt https://llmopt.metavert.io/mcpPlatform CLI: remote server
codex mcp add llm-optimizer --url https://llmopt.metavert.io/mcpConfig file
{
"mcpServers": {
"llm-optimizer": {
"url": "https://llmopt.metavert.io/mcp"
}
}
}Config file
{
"servers": {
"llm-optimizer": {
"type": "http",
"url": "https://llmopt.metavert.io/mcp"
}
}
}Platform CLI: remote server
gemini mcp add -t http llm-optimizer https://llmopt.metavert.io/mcpStep by step, with commands for each platform: How to add an MCP server to Claude Code, Codex, Cursor, VS Code, Gemini CLI and more
Questions about LLM Optimizer
How do I install LLM Optimizer in Claude Code?
Run claude mcp add --transport http llmopt https://llmopt.metavert.io/mcp. The server is remote, so there is nothing to install locally.
What does LLM Optimizer do?
It produces an AI Visibility Score across six analysis types: site content, video, Reddit, search visibility, model answers and aggregated brand intelligence. You get the score plus prioritized recommendations.
Does it need an API key?
The knowledge test queries Anthropic, OpenAI, Gemini and Grok, so it needs provider access on the service side. The other analysis types work from public data.
