# LLM Optimizer

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.

- MCP server · `type=mcp` · Claude Code, Codex, Cursor, GitHub Copilot, Windsurf (Devin Desktop), Cline, Zed, Gemini CLI · ★ 10
- Author: metavert-io
- License: MIT · policy=link-only · description only; the file stays with the author
- Source: https://github.com/metavert-io/llmopt
- Updated: 2026-10-17
- HTML: https://affpapa.org/en/nevibecode/mcp/llm-optimizer

## What it 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.

## Requirements
- Remote server: `https://llmopt.metavert.io/mcp`

## Install

### Claude Code (native platform)

**Platform CLI: remote server**
```bash
claude mcp add --transport http llmopt https://llmopt.metavert.io/mcp
```

### Codex

**Platform CLI: remote server**
```bash
codex mcp add llm-optimizer --url https://llmopt.metavert.io/mcp
```

### Cursor

**Config file**
File `.cursor/mcp.json`:
```json
{
    "mcpServers": {
        "llm-optimizer": {
            "url": "https://llmopt.metavert.io/mcp"
        }
    }
}
```

### GitHub Copilot

**Config file**
File `.vscode/mcp.json`:
```json
{
    "servers": {
        "llm-optimizer": {
            "type": "http",
            "url": "https://llmopt.metavert.io/mcp"
        }
    }
}
```

### Gemini CLI

**Platform CLI: remote server**
```bash
gemini mcp add -t http llm-optimizer https://llmopt.metavert.io/mcp
```

## When to use it
- 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
- 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

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