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GoAI Moat B2B Company Search

GoAI Moat B2B Company Search

Remote
goaimoat.comBusiness & Marketing0Updated 5w ago

About GoAI Moat B2B Company Search

GoAI Moat B2B Company Search is an MCP server for teams researching B2B companies by industry and location. It belongs to a suite of business intelligence servers for AI visibility and cross-border commerce.

Key features

  1. 01B2B company discovery by industry and location
  2. 02Part of a 13-server business intelligence suite
  3. 03Streamable HTTP server implementation

Use cases

  1. 01Finding companies in a target industry and location
  2. 02Researching businesses alongside cross-border market intelligence

MCP support

Exposes company-search capabilities to MCP clients as part of GoAI Moat's MCP server suite. The servers use streamable HTTP.

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://b2b.mcp.goaimoat.com/mcp
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

Tools

Details

From the project 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.

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

ServerRegistry IDWhat it does
AI Visibility Auditcom.goaimoat/ai-visibilityScore a brand's AI visibility 0-30 + 30-point checklist + fix priority
Competitor AI Signalscom.goaimoat/competitor-signals7 public signals + TSI score to detect a company's quiet AI adoption
Market Intel Briefcom.goaimoat/market-intel-briefSearch a weekly verified cross-border × AI intelligence library
Decision-Maker Lookupcom.goaimoat/decision-maker-lookupFind B2B decision makers and verify work emails
AI Content Studiocom.goaimoat/content-studioBilingual (EN/ZH) cross-border content generation
Brand Intel Memorycom.goaimoat/brand-intel-memoryPersistent business-intelligence memory for agents
Cross-border Profitcom.goaimoat/cross-border-profitCalculate cross-border e-commerce cost & profit
Export Compliancecom.goaimoat/export-complianceExport compliance checks (HS codes, restrictions)
IP Brand Protectioncom.goaimoat/ip-brand-protectionBrand/IP protection guidance
Pricing Strategycom.goaimoat/pricing-strategyPricing strategy for cross-border sellers
Product Selectioncom.goaimoat/product-selectionCross-border product selection research
Review Intelligencecom.goaimoat/review-intelligenceCustomer review intelligence
Social Media Strategycom.goaimoat/social-media-strategySocial media strategy for brands

Architecture

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

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.

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.