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designesy_report

Generate a unified design-intelligence report for a single URL — the synthesis capstone of the Designesy dynasty. Fires /score (42-check audit), /drift (12-check drift radar), and /readiness (10-check AI readiness) in parallel, then computes a weighted composite: score × 0.5 + drift × 0.3 + readiness × 0.2. One input, one output, one composite grade. Use this when you need a single holistic assessment instead of three separate scans, or when sharing a design-intelligence verdict (the report is the most shareable surface). When NOT to use: for just the audit score, use designesy_score; for just drift, use designesy_drift_score; for just AI readiness, use designesy_readiness_score. Executable — fires 3 internal APIs in parallel, each fetches the target URL. No browser needed. Returns JSON: { ok, url, compositeScore (0-100), compositeGrade (A-F), score { sub-result }, drift { sub-result }, readiness { sub-result }, totalChecks, totalPass, totalWarn, totalFail, totalSkip, checks[] (all checks across all engines, tagged with engine), synthesis[] (8 synthesis checks verifying the report ran correctly), appUrl (standalone interactive dashboard URL) }. Results cached ~24h per URL. MCP Apps: hosts that support io.modelcontextprotocol/ui render an interactive dashboard inline; others get the JSON plus an appUrl link.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesPublic URL to generate a design-intelligence report for.

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations present, the description carries the full disclosure burden and fully delivers. It reveals several important behaviors beyond the obvious 'generate a report': it is executable and fires three internal APIs in parallel, each fetching the target URL, no browser is needed, results are cached approximately 24 hours per URL, and the output can trigger an interactive dashboard on supporting hosts. These details materially affect invocation expectations and are not inferable from the schema.

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 long, but nearly every sentence adds distinct value: composite weighting, parallel execution, cache behavior, excluded siblings, return contract, and rendering behavior are all functionally important. The use of 'When NOT to use' and 'Executable' serve as explicit structural signposts, and the riskiest information (side effects and alternatives) appears early. Slight verbosity around the internal endpoint spellings is the minor cost of that transparency.

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?

For a single-parameter tool with no output schema, the description provides an unusually complete response contract: it enumerates every top-level field, explains the composite formula, describes sub-result structure, identifies total counts, and even notes the checks array engine tags and synthesis checks. It also covers execution semantics, caching, and rendering behavior. Nothing needed to invoke or interpret this tool is missing.

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?

The schema already describes the single 'url' parameter with 100% coverage, so the parameter semantics are fully documented there. The description adds only that the URL should be public, which is a modest but useful clarification. Per the rubric, baseline 3 applies when schema coverage is high and the description need not repeat parameter details.

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 opens with a specific verb and resource: 'Generate a unified design-intelligence report for a single URL.' It clearly distinguishes itself from sibling tools by naming the three sub-scans it combines and positioning itself as the shared, synthesis capstone of the Designesy family. The explicit exclusion of the separate score/drift/readiness tools removes ambiguity.

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 states when to use this tool ('when you need a single holistic assessment instead of three separate scans') and includes a dedicated 'When NOT to use' section naming designesy_score, designesy_drift_score, and designesy_readiness_score as the alternatives. This gives an agent direct routing instructions without requiring comparison of schema or sibling names.

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

A4.7/5.0
Disambiguation5/5

Each tool has a clearly scoped purpose, and the extensive 'When NOT to use' notices cleanly separate the many scoring variants (e.g., score, drift, readiness, monitor, tokens, motion, a11y). Even similar informational endpoints (contract, skill, llms) are differentiated by format and use case. No two tools appear to do the same thing.

Naming Consistency5/5

All tools follow a consistent 'designesy_' prefix, and scoring tools uniformly append '_score' (e.g., drift_score, tokens_score, monitor_score). Non-score tools use descriptive noun suffixes (catalog, contract, report, guardrails). The pattern is predictable and uniform throughout.

Tool Count4/5

At 17 tools, the set is slightly above the ideal 3-15 range, but the breadth of the design-system intelligence domain justifies the count. Each scoring variant targets a different artifact (live URL, token file, Lottie, temporal drift) and the informational endpoints serve distinct formats. The tool count is heavy but not bloated.

Completeness5/5

The toolset covers the full assessment lifecycle: full audit (score), drift and temporal governance (drift_score, monitor_score), AI readiness (readiness_score), token and motion validation (tokens_score, motion_score), accessibility framework (a11y_score), diff (compare), composite report (report), guardrails generation, and multiple discovery formats (catalog, contract, skill_md, llms). No obvious dead ends exist; each tool leads to a usable artifact or clear next step.