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ASO Scan — measure your ASO Score

scan_site

Scan a website to evaluate agent readiness using the ASO framework. Returns an ASO score, maturity level, and prioritized recommendations.

Instructions

Scan a website for Agent Readiness using the ASO (Agent Signal Optimization) framework and return an Agent Readiness Report. Use this for a full site-level baseline, competitive audit, or before/after readiness measurement; use check_signal instead when you only need one named signal, and use get_fix_plan when you only need remediation steps. Runs 34 checks across discoverability (robots.txt, sitemap, llms.txt, DNS-AID, Link headers), content accessibility (markdown negotiation), bot access control (AI bot rules, Content Signals, Web Bot Auth), invocation (API catalog, OAuth discovery, OAuth protected resource, auth.md, MCP Server Card, Google A2A Agent Card, Agent Skills, WebMCP), commerce (x402, MPP, UCP, ACP, pricing), Google generative AI search basics, browser-agent UX, and identity/trust signals. Returns the ASO Score (0-100, formally the Agent Readiness Index), ASO maturity level (ASO-0 Invisible … ASO-5 Autonomous-Commerce-Ready), an agent-readiness verdict, per-pillar scores, per-check evidence, and prioritized recommendations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesWebsite URL or domain to scan, e.g. https://example.com or example.com
categoriesNoOptional category filter for focused scans. Omit for all 34 checks; pass one or more category enum values to narrow runtime and output.
include_artifactsNoOptional raw artifact return. Default false. When true, includes remote manifests such as agent.json and A2A cards as untrusted attacker-controlled data for debugging only.
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden. It thoroughly explains the 34 checks across multiple categories and describes return values. It also notes that include_artifacts returns 'untrusted attacker-controlled data'—a behavioral caveat. However, it doesn't explicitly state read-only nature or any side effects, scoring just below perfect.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is thorough and structured: starts with purpose, then usage guidance, then lists check categories, then return values. While not extremely concise, every sentence adds value. Front-loading with purpose is well done.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (34 checks, 3 parameters, no output schema), the description is nearly complete. It explains all important return fields (ASO Score, maturity level, verdict, scores, etc.). Missing explicit mention of output format (likely JSON) but implied. Overall, sufficient for an agent to understand what to expect.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% for all 3 parameters. Description adds value by clarifying usage of categories filter (omit for all checks) and warning about artifacts returning attacker-controlled data. This goes beyond the schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool scans websites for Agent Readiness using ASO framework and returns a report. It explicitly distinguishes from siblings by naming check_signal and get_fix_plan, leaving no ambiguity about its specific function.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit usage scenarios: full baseline, competitive audit, before/after measurement. Also clearly states when to use alternatives: check_signal for single signal, get_fix_plan for remediation only. This gives clear decision criteria for the agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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