GoAI Moat Brand Mentions

Officialdeprecated

by jayniebingyu-cyber · Python

Search web mentions of a brand.

MCP Server: com.goaimoat/brand-mentions

The com.goaimoat/brand-mentions MCP server searches the web for mentions of a specified brand. It exposes functionality to retrieve mention results, based on the server’s purpose of “Search web mentions of a brand.”

🛠️ Key Features

Search web mentions of a brand

🚀 Use Cases

Find where a brand is mentioned across the web

⚡ Developer Benefits

Provides a server tool for brand mention search

⚠️ Limitations

Only described capability is searching web mentions of a brand; other behaviors are not specified

Topics

ai-agentsb2b-datacompany-dataemail-finderlead-generationllm-toolsmcpmcp-servermodel-context-protocolweb-scraping

README.md3 KBView on GitHub

GoAI Moat — MCP Servers

13 Model Context Protocol (MCP) servers that give AI agents the ability to audit "AI visibility", detect competitor AI adoption, and serve cross-border e-commerce teams. All listed under com.goaimoat/* on the official MCP Registry.

Being good is no longer enough — you have to be sayable by AI. These tools make that measurable.

Why

Buyers stopped searching — they ask AI. Being "cited by AI" is replacing "ranking #1":

65–68% of Google searches end without a click;
brands cited by AI earn ~35% more organic clicks and ~91% more paid clicks;
AI-referred visitors convert at 14.2% vs 2.8% for organic (≈5×).

The 13 servers

Server	Registry ID	What it does
AI Visibility Audit	`com.goaimoat/ai-visibility`	Score a brand's AI visibility 0-30 + 30-point checklist + fix priority
Competitor AI Signals	`com.goaimoat/competitor-signals`	7 public signals + TSI score to detect a company's quiet AI adoption
Market Intel Brief	`com.goaimoat/market-intel-brief`	Search a weekly verified cross-border × AI intelligence library
Decision-Maker Lookup	`com.goaimoat/decision-maker-lookup`	Find B2B decision makers and verify work emails
AI Content Studio	`com.goaimoat/content-studio`	Bilingual (EN/ZH) cross-border content generation
Brand Intel Memory	`com.goaimoat/brand-intel-memory`	Persistent business-intelligence memory for agents
Cross-border Profit	`com.goaimoat/cross-border-profit`	Calculate cross-border e-commerce cost & profit
Export Compliance	`com.goaimoat/export-compliance`	Export compliance checks (HS codes, restrictions)
IP Brand Protection	`com.goaimoat/ip-brand-protection`	Brand/IP protection guidance
Pricing Strategy	`com.goaimoat/pricing-strategy`	Pricing strategy for cross-border sellers
Product Selection	`com.goaimoat/product-selection`	Cross-border product selection research
Review Intelligence	`com.goaimoat/review-intelligence`	Customer review intelligence
Social Media Strategy	`com.goaimoat/social-media-strategy`	Social media strategy for brands

Architecture

Each server is a ~80-line FastMCP app over streamable HTTP:

python

from fastmcp import FastMCP
mcp = FastMCP(name="GoAI Moat — ...")

@mcp.tool()
def score_brand(brand: str, category: str) -> dict:
    """Score a brand's AI visibility 0-30 across 5 categories."""
    ...

mcp.run(transport="streamable-http", host="127.0.0.1", port=8000)

Open Core

The server code here is open source (MIT). The hosted endpoints and premium data are paid:

Hosted: point any MCP client at https://mcp.goaimoat.com/mcp (free tier = 1 audit/email).
Premium: unlimited audits + data reveal (decision-maker emails) via license — see goaimoat.com/mcp-catalog.html.

License

MIT — build on it, but the hosted service and proprietary data remain GoAI Moat's.

Install

Remote endpoint

Streamable HTTP

Hosted server - connect over the network, no local install.

url

https://mentions.mcp.goaimoat.com/mcp

Add to VS Code Add to Cursor

claude_desktop_config.json

json

{
  "mcpServers": {
    "brand-mentions": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://mentions.mcp.goaimoat.com/mcp"
      ]
    }
  }
}

Desktop config is stdio-only; this bridges via mcp-remote. Native remote: Settings > Connectors.