How does payment work?
The server uses the Stripe Machine-Payable Protocol. An agent calls the tool, receives a 402 Payment Required response with a Stripe URL, and a human completes the micro-payment to unlock the data.
@Spacemandomains
Basic information
High-intensity Generative Engine Optimization (GEO) auditing for AI agents. This tool allows agents to measure brand visibility and citation strength in LLM search results. It features a native 402 Payment Required handshake for $1.50 micro-payments per audit.
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"top-gun-audit": {
"command": "python",
"args": [
"mcp_server.py"
],
"env": {
"TOP_GUN_API_URL": "https://top-gun-live.vercel.app"
}
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
Top Gun Geo Lens is a high-intensity Generative Engine Optimization (GEO) tool that allows AI agents to measure brand visibility, citation strength, and indexing status within LLM search results. It is designed for autonomous agent payments using the Stripe Machine-Payable Protocol (MPP) at $1.50 per audit.
Install Python, clone the repository from https://github.com/Spacemandomains/top_gun_live, and add the server configuration to your MCP client (e.g., Claude Desktop). The single tool, audit_brand, scans LLM indices for brand mentions and returns a visibility score after a $1.50 micro-payment is completed via a Stripe URL.
The server uses the Stripe Machine-Payable Protocol. An agent calls the tool, receives a 402 Payment Required response with a Stripe URL, and a human completes the micro-payment to unlock the data.
Each audit costs $1.50 USD.
It provides one tool: `audit_brand`, which scans LLM indices for brand mentions and calculates a visibility score.
You need Python installed and an MCP-compatible client, such as Claude Desktop. No other dependencies are listed.
The data comes from scanning LLM indices (generative engine search results) for brand mentions.

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