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AI Visibility

by sharozdawa

Tracks brand mentions, sentiment, rankings, competitors, and visibility scores across four AI platforms.

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Install AI Visibility

Pick your client, then add it in one click or paste the config.

STDIONode.js
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "sharozdawa-ai-visibility": {
      "command": "npx",
      "args": [
        "-y",
        "ai-visibility-mcp"
      ]
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

Automated check passed— started and listed 6 tools correctly (15d ago).

Overview

The sharozdawa/ai-visibility MCP server tracks how brands appear across ChatGPT, Perplexity, Claude, and Gemini. Its tools report mention rates, result positions, sentiment, competitor presence, overall scores, and recommendations. Use it to inspect a brand, compare multiple brands, or examine a specific query and platform. The server runs through npx or a locally built Node.js process and supports Claude Desktop, Cursor, and MCP-compatible clients.

Use cases

  • Audit a brand's mentions across ChatGPT, Perplexity, Claude, and Gemini
  • Inspect a brand's position and sentiment for a specific query
  • Compare visibility scores for two to ten competing brands
  • Review prioritized recommendations for improving AI visibility

Key features

  • Multi-platform brand visibility checks
  • Mention rate, position, and sentiment analysis
  • Competitor detection
  • 0–100 visibility scoring with tier ratings
  • Side-by-side comparison for up to 10 brands
  • Prioritized improvement recommendations

Tools6

What AI Visibility lets your agent do. Expand a tool for its parameters.

Verified live~729 tokens of context

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 1d ago

Recent health check succeeded.

5/6 checks healthy over the last 79d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 11d ago via OSV.dev · ai-visibility-mcp (npm)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars11Stargazers on the source repository.
npm downloads186Package downloads in the last 30 days.
Last commit7mo agoMost recent push to the default branch.
Availability100%Our rolling endpoint + install checks that succeeded.
Install checkPassedOur sandbox started it and listed its tools.
Tools exposed6Callable tools this server registers over MCP.
Directory activity3 viewsConfig copies, upvotes, and views on AllMCPs.

How AI Visibility works

What sharozdawa/ai-visibility MCP server does

The sharozdawa/ai-visibility MCP server provides tools for evaluating brand visibility in four AI platforms: ChatGPT, Perplexity, Claude, and Gemini. It focuses on whether a brand is mentioned, where it appears, how it is described, and which competing brands appear in the same results.

The available analysis covers both broad brand checks and individual query checks. A broad check returns platform-level results, mention rates, positions, sentiment, and competitor information. A single-query check narrows the result to one query and one platform, returning the mention status, position, context snippet, and sentiment.

How it works

The server calculates an overall visibility score from 0 to 100 using three weighted inputs:

  • Mention rate contributes 40 percent.
  • Position quality contributes 30 percent.
  • Sentiment contributes 30 percent.

Scores are grouped into five tiers: Excellent, Good, Moderate, Low, and Very Low. The scoring model treats earlier mentions as better positions, with first place ranked above later positions. Sentiment is categorized as positive, neutral, or negative.

The sharozdawa/ai-visibility MCP server also provides side-by-side comparison for two to ten brands. Its platform listing describes how each supported AI system sources and presents brand information. Results use seeded randomness, so the same brand can produce reproducible results rather than changing unpredictably between identical runs.

Setup and configuration

The quickest setup uses the published npm package through npx:

Terminal
npx ai-visibility-mcp

For Claude Desktop, add an MCP entry that runs npx with -y ai-visibility-mcp as its arguments. Cursor uses the same command structure in .cursor/mcp.json.

Self-hosting is also supported. Clone the repository, enter mcp-server, install dependencies, build the project, and run node dist/index.js. An MCP client can then be configured to launch that local JavaScript file instead of using npx.

The repository additionally includes a Next.js dashboard. Running that dashboard requires a .env file with DATABASE_URL, Prisma database setup, and the project’s development command; this configuration is separate from the documented MCP server launch.

Tools and capabilities

The server exposes six tools:

  • check_brand_visibility reviews a brand across supported platforms and returns detailed visibility, position, sentiment, mention-rate, and competitor data.
  • check_single_query checks one brand, query, and platform for mention status, position, context, and sentiment.
  • get_visibility_score returns the 0–100 score, tier, platform breakdowns, and improvement recommendations.
  • compare_brands ranks two to ten brands and provides per-platform scores.
  • get_recommendations produces prioritized actions based on the current visibility score.
  • list_platforms describes the supported platforms and their information sources and presentation models.

Limitations and notes

The documented platform set is limited to ChatGPT, Perplexity, Claude, and Gemini. Brand comparisons accept between two and ten brands. Because the project uses seeded randomness for consistent results, outputs should be understood as reproducible tracker results rather than an indication that every response from an AI platform has been continuously monitored.

Read the full README View source on GitHub

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Frequently Asked Questions about AI Visibility

sharozdawa/ai-visibility is an MCP server that connects brand visibility analysis to ChatGPT, Perplexity, Claude, and Gemini. Its main tools check brand visibility, inspect a single query, calculate scores, compare brands, generate recommendations, and list supported platforms with sourcing details.

Install it by running `npx ai-visibility-mcp`, or configure an MCP client to run `npx` with `-y ai-visibility-mcp` as arguments. The README provides configuration examples for Claude Desktop and Cursor. You can also clone the repository and run the built Node.js server locally.

Yes, sharozdawa/ai-visibility works with Cursor through its MCP settings. Add an entry to `.cursor/mcp.json` that launches `npx` with `-y ai-visibility-mcp`, then use the server’s tools from Cursor.

The score combines mention rate at 40 percent, position quality at 30 percent, and sentiment at 30 percent. It ranges from 0 to 100 and is assigned an Excellent, Good, Moderate, Low, or Very Low tier.

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