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AIScan MCP Readiness Score
x402
AIScan MCP Readiness Score is a paid API for AI agents from api.getaiscan.app, paid per call via x402, $0.2/call, status unknown (last checked 2026-09-15).
Audits a website's MCP (Model Context Protocol) readiness as part of an AI visibility assessment
Price / call$0.2
Success—
StatusUnknown
How this service works
AIScan audits any website for AI visibility in 60 seconds. 4 scores: AEO, GEO, Agent Readiness, MCP Readiness. Plus Brand Visibility: how often AI actually names your brand. The only scanner with MCP Readiness.
Output
Returns the capability name that was executed, the analyzed site URL, and payment settlement info. The MCP readiness score for the given website is the core output — indicating how well the site supports Model Context Protocol standards for AI agent discoverability.
Example use cases
Example prompt
“Can you check how MCP-ready my website is? Run an AIScan MCP readiness audit on https://mycompany.com and tell me the score.”
Technical details
Endpoint
POSThttps://api.getaiscan.app/api/agent/score_mcpWhen to prefer
Use this endpoint when you specifically need to evaluate a website's MCP (Model Context Protocol) readiness — i.e., how well it is set up to be discovered and used by AI agents via MCP standards. It is distinct from the broader AEO/GEO/Agent Readiness scores offered by sibling endpoints on the same platform. Choose this when the user explicitly wants to know about MCP compliance or agent-protocol-level discoverability rather than general AI search visibility.
Side effects
- Invalid or unreachable URL returns an error
- Missing required 'url' field causes validation failure
- Payment not settled results in 402 response before audit runs
- Timeout if target site is too slow to respond during analysis
Input
{
"url": "https://example.com"
}Schema · Request body
| Field | Type | Description |
|---|---|---|
| urlrequired | string | Full URL of the website to analyze, e.g. https://example.com |
Output
{
"url": "https://example.com",
"grade": "F",
"score": 15,
"checks": [
{
"fix": "Create /.well-known/mcp.json with MCP server configuration including name, version, and available tools",
"name": "/.well-known/mcp.json endpoint",
"status": "fail"
},
{
"fix": "Add comprehensive server card metadata with description, capabilities, and authentication details",
"name": "MCP Server Card quality",
"status": "fail"
},
{
"fix": "Create /.well-known/ai-plugin.json with plugin manifest following OpenAI plugin specification",
"name": "/.well-known/ai-plugin.json endpoint",
"status": "fail"
},
{
"fix": "Create /llms.txt file with dedicated MCP section describing available tools and endpoints",
"name": "llms.txt with MCP section",
"status": "fail"
},
{
"fix": "Add /agent.json or /.well-known/agent.json with agent capabilities and MCP server details",
"name": "agent.json configuration",
"status": "fail"
},
{
"fix": "Add structured metadata (JSON-LD or meta tags) describing available MCP tools and their parameters",
"name": "Tool descriptions in metadata",
"status": "fail"
},
{
"fix": "Implement JSON-LD schema with WebAPI or SoftwareApplication type describing MCP capabilities",
"name": "Structured data for AI discoverability",
"status": "fail"
},
{
"fix": "Add meta tags like 'mcp:enabled', 'mcp:server-url', or 'ai:capabilities' for basic discoverability",
"name": "MCP-related meta tags",
"status": "fail"
}
],
"payment": {
"tx": "0x0f929c8629012e33da9307c2cbb5151a72e5cb16c7dee5a3b8d5aa5dc70d4b45",
"flow": "cdp_facilitator",
"amount": 0.2,
"settled": true,
"currency": "USDC",
"x402Version": 2
},
"summary": "This website has no MCP readiness implementation. None of the standard AI discoverability mechanisms (/.well-known/mcp.json, llms.txt, agent.json, or structured metadata) are present.",
"capability": "score_mcp"
}Schema · Response
| Field | Type | Description |
|---|---|---|
| url | string | Analyzed site URL |
| payment | object | Payment settlement info |
| capabilityrequired | string | Capability that was executed |
Tags
x402
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