visibilityradar-mcp
README.md
# visibilityradar-mcp
> Analyze how AI models see your brand — directly from Claude Desktop, Cursor, Windsurf, or any MCP-compatible AI assistant.
[](https://www.npmjs.com/package/visibilityradar-mcp)
[](https://opensource.org/licenses/MIT)
[](https://glama.ai/mcp/servers/sarefe12-sudo/visibilityradar-mcp)
## What is this?
**VisibilityRadar** measures how often and how positively AI models (Claude, GPT-4o, Gemini, Perplexity, Grok, DeepSeek) mention your brand. This MCP server brings that analysis directly into your AI assistant — no browser needed.
Ask Claude: *"How visible is my brand on AI models compared to my competitors?"* and get an instant, structured report.
## Tools
| Tool | Description |
|---|---|
| `analyze_brand` | Run a full AI visibility analysis: overall score, per-model scores, sentiment, competitors, top recommendations |
| `get_brand_history` | Fetch past analysis results and score trends for a brand |
## Requirements
- **Pro or Agency plan** on [VisibilityRadar](https://visibilityradar.ai/pricing)
- An API key from [Account Settings → MCP API Keys](https://visibilityradar.ai/profile)
## Setup
### 1. Get your API key
Go to [visibilityradar.ai/profile](https://visibilityradar.ai/profile) → scroll to **MCP API Keys** → click **Generate Key**.
### 2. Configure your AI assistant
#### Claude Desktop
Edit `~/Library/Application Support/Claude/claude_desktop_config.json` (macOS) or `%APPDATA%\Claude\claude_desktop_config.json` (Windows):
```json
{
"mcpServers": {
"visibilityradar": {
"command": "npx",
"args": ["visibilityradar-mcp"],
"env": {
"VR_API_KEY": "vr_your_api_key_here"
}
}
}
}
```
#### Cursor
Edit `~/.cursor/mcp.json`:
```json
{
"mcpServers": {
"visibilityradar": {
"command": "npx",
"args": ["visibilityradar-mcp"],
"env": {
"VR_API_KEY": "vr_your_api_key_here"
}
}
}
}
```
#### Windsurf
Edit `~/.codeium/windsurf/mcp_config.json`:
```json
{
"mcpServers": {
"visibilityradar": {
"command": "npx",
"args": ["visibilityradar-mcp"],
"env": {
"VR_API_KEY": "vr_your_api_key_here"
}
}
}
}
```
### 3. Restart your AI assistant
After saving the config, restart Claude Desktop / Cursor / Windsurf. You should see VisibilityRadar listed as a connected tool.
## Example Usage
Once connected, just ask your AI assistant:
```
Analyze the AI visibility of "Notion" in the US market, compare against Obsidian and Roam Research
```
```
What is the brand history for "Linear"?
```
```
How does "Shopify" score across AI models compared to WooCommerce?
```
### Example Output
```
# AI Visibility Report: Notion
**Overall Score: 74/100** (Strong)
**Market:** US
📊 Sentiment: 68% positive · 24% neutral · 8% negative
## Per-Model Scores
• Claude: 82/100
• GPT-4o: 78/100
• Gemini: 71/100
• Perplexity: 69/100
• Grok: 74/100
• DeepSeek: 66/100
## Competitor Comparison
• Obsidian: 58/100
• Roam Research: 41/100
## Top Recommendations
1. [HIGH] Build a stronger Wikipedia presence with product comparisons
2. [HIGH] Earn more coverage on tech publications indexed by Perplexity
3. [MEDIUM] Increase presence on X/Twitter for Grok visibility
---
📊 Full report, playbook & content strategy: https://visibilityradar.ai/dashboard
```
## Rate Limits
| Plan | Daily MCP analyses | Monthly analyses |
|---|---|---|
| Pro | 5/day | 10/month |
| Agency | 20/day | Unlimited |
Every `analyze_brand` call via MCP counts as one analysis and is saved to your dashboard automatically.
## API Endpoints
The MCP server calls the following VisibilityRadar API endpoints:
- `POST https://visibilityradar.ai/api/mcp/analyze` — Run analysis
- `GET https://visibilityradar.ai/api/mcp/history` — Fetch history
Authentication uses the `x-api-key` header with your API key.
## Links
- [VisibilityRadar](https://visibilityradar.ai)
- [Pricing](https://visibilityradar.ai/pricing)
- [FAQ](https://visibilityradar.ai/faq)
- [npm package](https://www.npmjs.com/package/visibilityradar-mcp)
## License
MIT
TDQS
A3.7/5.0
Scored across 2 tools
Disambiguation5/5
The two tools have clearly distinct purposes: analyze_brand performs new analyses, while get_brand_history retrieves past results. No overlap or ambiguity.
Naming Consistency5/5
Both tools follow a consistent verb_noun pattern (analyze_brand, get_brand_history), making the tool set predictable and easy to navigate.
Tool Count3/5
With only 2 tools, the server is on the lower end of the typical range for a dedicated service. While the core functionality is covered, the count feels slightly thin for a dashboard scenario.
Completeness2/5
The set covers analysis creation and history retrieval but lacks basic CRUD operations such as listing all brands, updating analyses, or deleting entries, which are significant gaps for a dashboard.
Maintenance
ActivityMaintained
ResponsivenessSyncing