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ASO Score MCP

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ASO Scan — measure your ASO Score

scan_site

Scans a website for agent-readiness using the ASO framework, returning a score report with 34 checks across discoverability, content accessibility, bot access control, and more.

Instructions

Scan a website for Agent Readiness using the ASO (Agent Signal Optimization) framework and return an ASO Score 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 fully covers behavior: it runs 34 checks, returns ASO Score, maturity level, verdict, per-pillar scores, evidence, and recommendations. It also notes that include_artifacts returns untrusted data. It is thorough, though it does not explicitly state it is read-only.

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

Conciseness5/5

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

The description is a single paragraph of four well-structured sentences. It front-loads the core purpose, then usage guidance, then details, then output summary. No wasted words.

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 tool's complexity (34 checks, multiple output components) and no output schema, the description adequately lists return items. It could mention the output format but is otherwise complete for an agent to understand what the tool returns.

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%, so baseline is 3. The description adds value by explaining the purpose of categories (e.g., Discoverability) and the caution about include_artifacts returning untrusted data, which goes beyond the schema.

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?

The description clearly states the tool's purpose: scanning a website using the ASO framework and returning an ASO Score Report. It uses specific verbs and resources, and differentiates from sibling tools like check_signal and get_fix_plan.

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?

The description explicitly provides when to use this tool (full baseline, competitive audit, before/after measurement) and when to use alternatives (check_signal for single signal, get_fix_plan for remediation only). This gives clear context and exclusions.

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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