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crypto-yannso

viraill-mcp

agentic_scan

Evaluate a website's Agentic Commerce Readiness with a 100-point score across five pillars, then generate remediation code for identified gaps.

Instructions

Audit a website's Agentic Commerce Readiness (/100 score across 5 pillars: Crawl/Ingestion, OpenAPI, Schema.org, x402 settlement, 8 distribution channels) and generate remediation code.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesTarget URL to scan
nameNoOptional brand or SaaS name
remediateNoGenerate full remediation code pack (llms.txt, agent.json, openapi.json)
Behavior3/5

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

With no annotations provided, the description carries the full behavioral burden. It discloses the audit outcome and remediation code generation, but it does not clarify that remediation is controlled by the remediate parameter or mention side effects like network access to the requested site.

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 entire description is one tight sentence that leads with the core action, includes key measurement details in parentheses, and avoids any filler. Every phrase earns its place.

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?

For a tool without an output schema, this description explains what the result is (score across five pillars) and what remediation can result. It does not thoroughly address the optional remediate flag, but the parameter schema documents that, making the definition sufficiently complete.

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

Parameters3/5

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

Input schema describes all three parameters (url, name, remediate) in detail, so schema coverage is 100%. The description adds domain context but no additional parameter-level semantics, which matches the baseline expectation.

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 uses a specific verb ('Audit') and identifies the exact resource ('website's Agentic Commerce Readiness'), defines the /100 scoring across five pillars, and mentions remediation code output. This strongly differentiates it from sibling tools like geo_audit and geo_social_generate.

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

Usage Guidelines4/5

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

The clearbly scopes usage to auditing agentic commerce readiness, giving an agent strong context. However, it doesn't explicitly mention when to use alternatives or state exclusions, though the sibling names suggest different domains.

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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