Grades any storefront URL A-F on 20 weighted conversion checks. $1.00/call via x402.
Scored across 1 tool
There is only a single tool, so there is no possibility of confusion or misselection between tools. Its stated purpose (audit a storefront for AI-agent readiness) is unambiguous.
The lone name grade_storefront follows a clean verb_noun snake_case pattern. With one tool there is no convention mixing to worry about.
The domain is essentially one monolithic action — run the audit and return a report — so one tool is defensible rather than artificial. It is slightly thin in that supporting operations (e.g. retrieving a past report or re-checking a single category) are absent, but the scope is narrow enough that a single tool does not feel excessive or mismatched.
The tool claims full coverage of its own domain: 20 weighted checks across product, discovery, conversation, transaction, and trust categories, returning scores, grades, per-check evidence, and prioritized fixes. The only minor gap is the lack of any lifecycle/supporting operations such as storing, listing, or comparing prior audits.
Audit an ecommerce storefront for AI-agent shopping readiness. Fetches the store server-side and runs 20 weighted checks across product intelligence (schema.org Product/Offer price, availability, variants, GTIN/MPN), AI discovery (robots.txt, llms.txt, agents.md, sitemap.xml, UCP manifest, MCP endpoint), conversation readiness (contact info, FAQ/returns, site search), transaction readiness (HTTPS, checkout signals, tax/shipping as values), and trust (business identity, policies). Returns a machine-readable JSON report: 0-100 score, A-F grade, per-check pass/partial/fail/unknown with evidence, and the top 3 prioritized fixes. Cost: $1.00 USDC on Base per call, paid via x402 — the caller's agent pays from its owner's approved wallet; no API key needed. Use this to decide whether a store is ready for AI shopping agents, or to generate the audit behind a readiness report for a merchant.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Storefront URL to grade, e.g. https://example-store.com |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does so: it discloses server-side fetching, the scoring methodology (20 weighted checks), the return shape, the $1.00 USDC-on-Base x402 payment flow funded from the owner's approved wallet, and that no API key is required. Cost and auth mechanics are exactly the traits an agent needs before invoking.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Purpose is front-loaded in the first sentence, followed by capability detail, cost, and usage. The enumerated check categories are dense but each carries signal for a scoring tool; the only mild bloat is the length of the category list, which is still informative rather than redundant.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description specifies the return payload (0-100 score, A-F grade, per-check pass/partial/fail/unknown with evidence, top 3 fixes), plus the payment precondition. Nothing an agent needs to call this correctly or interpret results is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Only one parameter with 100% schema description coverage, so the schema already documents 'url' fully. The description adds that the store is fetched server-side, which slightly clarifies handling, but contributes no format or validation detail beyond the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb+resource ('Audit an ecommerce storefront for AI-agent shopping readiness') and enumerates the exact check categories performed. No sibling exists, and the agent can tell precisely what the tool produces without opening anything else.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Closes with explicit when-to-use framing: deciding whether a store is ready for AI shopping agents, or generating the audit behind a merchant readiness report. There are no alternative tools to route against, so no exclusions are needed, but the guidance is clear and actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
grade_storefrontScores any URL's AI-search visibility with concrete fixes. $1.50/call via x402.
The website checker for AI agents. One call: verdict, score out of 100, fix list.
Turn any URL into clean, structured, agent-ready content. $0.02/call via x402 on Base.
Check a deployed web app for security headers, SEO, accessibility and performance, with a grade.
Add one secure layer between your agents and this server.
