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Motion Menu

by io.github.dev55acc-ai

verify_repair

Re-audit a page you just rewrote from audit_page's prescriptions and get a pass/fail verdict: fresh ai_shaped_score, the delta from the prior audit (pass baseline_score or the whole prior audit_page result as baseline), and any tells STILL present, each with a concrete fix instruction. FREE, no key required.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
htmlYesthe rewritten page's full HTML
baselineNooptional — the whole prior audit_page result, for a per-section tell-count delta in addition to the overall score delta; baseline_score is derived from it if omitted
baseline_scoreNooptional — the ai_shaped_score from the prior audit_page call, for a pass/fail delta

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It clearly states the return value (score, delta, tells with fix instructions) and adds useful context about being free and requiring no key. It does not go into error handling or rate limits, but it covers the essential behavior well.

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 a single, dense sentence that packs many clauses and a colon-separated list. It is economical with words, but the structure is slightly overloaded; breaking it into two sentences would improve readability. Still, every phrase earns its place.

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

Completeness5/5

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

Given the absence of an output schema, the description adequately explains the return values (fresh score, delta, residual tells with fix instructions). It also covers prerequisites (rewriting from audit_page), usage pattern, and cost, making it complete for an AI agent to decide when and how to invoke it.

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

Parameters5/5

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

The schema already describes parameters well (100% coverage), but the description adds crucial relationship semantics: it explains that baseline and baseline_score are alternative ways to pass the prior audit, and that baseline_score is derived from baseline when omitted. This goes beyond the schema's simple 'optional' labels.

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 uses a specific verb ('re-audit') and resource ('a page you just rewrote'), clearly distinguishing it from the sibling audit_page tool. It also states the context (from audit_page's prescriptions) and the outcome (pass/fail verdict), making the purpose unmistakable.

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 description implies when to use the tool: after rewriting a page based on audit_page's prescriptions. It gives clear contextual guidance but does not explicitly name alternative tools or list when-not-to-use conditions, so it falls short of a perfect 5.

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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TDQS

A4.5/5.0
Disambiguation5/5

Each tool targets a distinct stage of the workflow: discovery (list_patterns, recommend_patterns), selection (get_pattern), validation (validate_combination), construction (build_page), quality assurance (audit_page, verify_repair), configuration (get_kit, get_design_digest), and maintenance (check_updates). audit_page and verify_repair are related but clearly differentiated by baseline comparison and pass/fail verdicts.

Naming Consistency5/5

All tools follow an identical verb_noun snake_case pattern (audit_page, build_page, list_patterns, validate_combination, etc.). The verbs are specific and descriptive, and the nouns clearly indicate the target resource. Naming is perfectly uniform.

Tool Count5/5

With 10 tools, the server is well-scoped for its purpose. It covers the full lifecycle of building and auditing Motion Menu pages without redundancy or unnecessary sprawl. Each tool earns its place in a cohesive workflow, and the count fits the domain perfectly.

Completeness5/5

The tool surface is comprehensive for the stated purpose: from discovering patterns (list_patterns, recommend_patterns) to fetching code (get_pattern), validating combinations (validate_combination), designing pages (build_page), auditing (audit_page), repairing (verify_repair), and tracking updates (check_updates). The only missing operation might be a direct 'create pattern' tool, but that falls outside the server's intended scope of using existing patterns.

Resources