Allows running VisibilityRadar brand analysis directly from Windsurf IDE (a Codeium product).
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@visibilityradar-mcp Analyze AI visibility for Notion"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Analyze how AI models see your brand — directly from Claude Desktop, Cursor, Windsurf, or any MCP-compatible AI assistant.
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.
Related MCP server: ai-visibility-mcp
Tool | Description |
| Run a full AI visibility analysis: overall score, per-model scores, sentiment, competitors, top recommendations |
| Fetch past analysis results and score trends for a brand |
Pro or Agency plan on VisibilityRadar
An API key from Account Settings → MCP API Keys
Go to visibilityradar.ai/profile → scroll to MCP API Keys → click Generate Key.
Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):
{
"mcpServers": {
"visibilityradar": {
"command": "npx",
"args": ["visibilityradar-mcp"],
"env": {
"VR_API_KEY": "vr_your_api_key_here"
}
}
}
}Edit ~/.cursor/mcp.json:
{
"mcpServers": {
"visibilityradar": {
"command": "npx",
"args": ["visibilityradar-mcp"],
"env": {
"VR_API_KEY": "vr_your_api_key_here"
}
}
}
}Edit ~/.codeium/windsurf/mcp_config.json:
{
"mcpServers": {
"visibilityradar": {
"command": "npx",
"args": ["visibilityradar-mcp"],
"env": {
"VR_API_KEY": "vr_your_api_key_here"
}
}
}
}After saving the config, restart Claude Desktop / Cursor / Windsurf. You should see VisibilityRadar listed as a connected tool.
Once connected, just ask your AI assistant:
Analyze the AI visibility of "Notion" in the US market, compare against Obsidian and Roam ResearchWhat is the brand history for "Linear"?How does "Shopify" score across AI models compared to WooCommerce?# 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/dashboardPlan | 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.
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.
MIT
Analyze how visible a brand is across AI models (Claude, GPT-4o, Gemini, Perplexity, Grok, DeepSeek). Returns an overall score, per-model scores, sentiment analysis, competitor comparison, and top recommendations. Results are also saved to the VisibilityRadar dashboard.
| Name | Required | Description | Default |
|---|---|---|---|
| brand | Yes | The brand name to analyze (e.g. "Nike", "Apple", "Notion") | |
| market | No | Target market or region (e.g. "global", "US", "TR", "UK"). Defaults to "global". | global |
| competitors | No | Optional list of competitor brand names to compare against (max 3) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It discloses that results are saved to a dashboard (side effect) and outlines return data types. However, it does not mention auth requirements, rate limits, or failure conditions. For a tool with no annotations, this is adequate but not comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences with no filler. The first sentence immediately states the action and scope (analyze brand across AI models) and the second lists outputs and side effect. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description lists return components (overall score, per-model scores, etc.) but does not explain the score range or calculation methodology. For a complex analysis tool, more detail on output semantics would improve completeness. The side effect (dashboard save) is noted.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides good descriptions for all three parameters (100% coverage). The description adds value by explaining how competitors are used ('competitor comparison') and that market defaults to 'global'. It reinforces the purpose of each parameter without being redundant.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it analyzes brand visibility across multiple specific AI models (Claude, GPT-4o, etc.) and returns structured outputs like an overall score, per-model scores, sentiment, competitor comparison, and recommendations. It also mentions a side effect (saving to dashboard). This differentiates it from the sibling tool get_brand_history, which is likely historical in nature.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not explicitly state when to use this tool versus the sibling get_brand_history. It implies use for current visibility analysis across models, but lacks exclusions or alternative scenarios. The sibling tool name suggests historical data, but no direct guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the analysis history for a specific brand from the VisibilityRadar dashboard. Shows score trends over time.
| Name | Required | Description | Default |
|---|---|---|---|
| brand | Yes | The brand name to look up history for |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the full burden of disclosure. It states the tool 'gets history' and 'shows score trends,' which implies a read operation. However, it does not mention any potential side effects, required permissions, data freshness, or limitations like history depth. This is adequate for a simple retrieval but could be more transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences with no redundancy. It front-loads the action and resource, then adds a clarifying detail about the output. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool with no output schema, the description is incomplete. It tells what it does but not what the returned data looks like (e.g., format, time range, metrics). The agent lacks information to confidently process the result.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents the 'brand' parameter. The tool description adds the context of 'history' and 'score trends,' but this does not enhance understanding of the parameter beyond what the schema provides. Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Get' and clearly identifies the resource 'analysis history for a specific brand'. It also mentions 'Shows score trends over time,' which adds specificity. The sibling tool analyze_brand suggests a different function (analysis vs history), so this tool is well-distinguished.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus the sibling analyze_brand. There is no mention of prerequisites, exclusions, or alternative scenarios. The agent has to infer based on the name and description alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.0analyze_brandget_brand_historyThe two tools have clearly distinct purposes: analyze_brand performs new analyses, while get_brand_history retrieves past results. No overlap or ambiguity.
Both tools follow a consistent verb_noun pattern (analyze_brand, get_brand_history), making the tool set predictable and easy to navigate.
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.
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.
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