# visibility [Health: Active]

**Category:** 💻 Developer Tools  
**Repository:** https://frontstat.com/mcp  
**npm Downloads (last month):** 510  
**Views:** 0  
**Installs:** 0  
**Upvotes:** 0  
**Directory Page:** https://allmcps.com/mcp/visibility

## Description
See whether ChatGPT, Claude and Gemini recommend a startup's product, and who they name instead.

## Claude Desktop Quick Installation
Remote MCP endpoint (confidence: medium). Install path inferred — verify against the README before running it. Add as a URL/SSE server in your client:

```json
"mcpServers": {
  "visibility": {
    "url": "https://frontstat.com/mcp"
  }
}
```

## Documentation

## What the visibility MCP server does

The visibility MCP server is described as a way to check how three AI systems—ChatGPT, Claude, and Gemini—respond to a startup product. Its central question is whether a model recommends that product. The service also reports which other products the models name instead, making it possible to compare direct recommendations with alternatives surfaced in the same evaluation.

This is relevant for teams assessing whether a startup product appears in AI-generated recommendations. It can help organize a comparison across the three named model providers rather than relying on a single model’s response. The supplied material does not define the question format, evaluation scope, result structure, or whether checks run against live model responses or another data source.

## How it works

The available description indicates a two-part result: recommendation status for the startup’s product and the names of competing or alternative products returned by ChatGPT, Claude, or Gemini. The exact workflow is not documented in the supplied material. There is no information about prompts, authentication, polling, response timing, model versions, or how results are stored.

Use the visibility MCP server when the decision depends on whether a product is mentioned favorably by the named AI systems. Treat comparisons as specific to the inputs and conditions used by the service, since the listing does not state how queries are constructed or how often results are refreshed.

## Tools and capabilities

Based on the available description, the documented capability is checking AI recommendations for a startup product and identifying alternatives named by the models. No individual MCP tool names, parameters, return fields, or invocation examples are provided.

The visibility MCP server is therefore best evaluated as a recommendation-monitoring service at the capability level. Developers needing a precise integration plan should obtain the project’s tool schema and usage instructions before implementation.

## Limitations and notes

The README is unavailable, so installation steps, transport type, client compatibility, environment variables, authentication requirements, licensing, and operational limits cannot be confirmed. The source description names ChatGPT, Claude, and Gemini, but it does not specify supported model variants or whether users can select particular versions.

The listing also does not explain how the service distinguishes a recommendation from a neutral mention, how it handles ambiguous product names, or how it presents alternatives. Those details matter when using results for repeatable visibility tracking or product comparisons. Confirm them in the project documentation before treating outputs as a standardized measurement.

_Full upstream README: https://allmcps.com/mcp/visibility/readme_