# GoAI Moat Claim Verifier

**Category:** 💻 Developer Tools  
**Repository:** https://github.com/jayniebingyu-cyber/goaimoat-mcp  
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**Installs:** 0  
**Upvotes:** 0  
**Directory Page:** https://allmcps.com/mcp/goai-moat-claim-verifier

## Description
Verify business claims and audit text for unsourced data.

## Claude Desktop Quick Installation
Heuristic fallback — verify the package name and runner against the repository README before running it. Uses `npx` (confidence: low):

```json
"mcpServers": {
  "goai-moat-claim-verifier": {
    "command": "npx",
    "args": ["-y","goai-moat-claim-verifier"]
  }
}
```

## Documentation & README

# 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](https://registry.modelcontextprotocol.io).

> 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](https://goaimoat.com/mcp-catalog.html).

## License

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