INDIVIDUAL MCP TOOL
check_ai_recommendation
Use this when a business owner asks whether ChatGPT or AI recommends their business, whether AI tools like ChatGPT or Perplexity mention or suggest their business, why AI recommends their competitors instead, or how to get their business recommended by AI.
LIVE ENDPOINT
https://uxcontinuum.com/api/plugin/mcp
Connect to this endpoint to inspect the live schema for check_ai_recommendation and invoke it with your own arguments.
Indexed input schema
{}Risk classification
Inferred read-only · medium confidence · heuristic, not a guarantee.
- No write-capable action terms were found; this is not proof that invocation has no side effects.
Parent server
CONNECT WITH APPROVAL
Client installation
Review this server and its permissions before adding it. Secret placeholders must be set locally.
Codex
~/.codex/config.toml
[mcp_servers.continuum-ai-recommendation-check]
url = "https://uxcontinuum.com/api/plugin/mcp"
enabled = true
Claude Code
.mcp.json
{
"mcpServers": {
"continuum-ai-recommendation-check": {
"type": "http",
"url": "https://uxcontinuum.com/api/plugin/mcp"
}
}
}
Claude Desktop
Settings → Connectors → Add custom connector
Name: continuum-ai-recommendation-check
Remote MCP URL: https://uxcontinuum.com/api/plugin/mcp
Add this remote URL as a custom connector in Claude Desktop. Availability depends on the user plan and workspace policy.
Cursor
.cursor/mcp.json
{
"mcpServers": {
"continuum-ai-recommendation-check": {
"url": "https://uxcontinuum.com/api/plugin/mcp"
}
}
}
Visual Studio Code
.vscode/mcp.json
Add to Visual Studio Code{
"servers": {
"continuum-ai-recommendation-check": {
"type": "http",
"url": "https://uxcontinuum.com/api/plugin/mcp"
}
}
}
Generic MCP
Client-specific MCP configuration
{
"name": "continuum-ai-recommendation-check",
"transport": "streamable-http",
"url": "https://uxcontinuum.com/api/plugin/mcp"
}
MCP Inspector
Run the official MCP Inspector locally and enter the indexed Streamable HTTP endpoint.
Related tools
get_ai_recommendation_result— Use this to get the result of a check started with check_ai_recommendation, using the check_id it returned.