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Designesy

by io.github.LE-VAI
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Build composite design report

designesy_report
Read-onlyIdempotent

Generate a unified design-intelligence report for a single URL, the synthesis capstone. Fires /score (45-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. The full result is large: about 75 to 100 KB of JSON on the ten sites measured, on the order of 20,000 tokens or more, because it carries each engine's whole result beside the merged check list. To trim it, pass detail "summary": the same keys with fewer rows, about 4 to 31 KB on the same sites. It keeps the composite, the totals, each engine's score, grade and counts, and only the checks and synthesis entries that did not PASS; omitted counts what was left out. detail "full" (the default) returns everything. Returns JSON: { ok, url, compositeScore (0-100), compositeGrade (A-F), score { sub-result }, drift { sub-result }, readiness { sub-result }, totalChecks, totalPass, totalWarn, totalFail, totalSkip, totalManual, checks[] (all checks across all engines, tagged with engine), synthesis[] (8 synthesis checks verifying the report ran correctly), appUrl (standalone interactive dashboard URL) }; with detail "summary" it adds detail and omitted. 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

NameRequiredDescriptionDefault
urlYesPublic URL to generate a design-intelligence report for.
detailNoOptional: "full" (the default) returns everything; "summary" returns the composite, the totals, each engine's score, grade and counts, and only the checks that did not PASS.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / detail
      Added value: +{
+  "description": "Optional: \"full\" (the default) returns everything; \"summary\" returns the composite, the totals, each engine's score, grade and counts, and only the checks that did not PASS.",
+  "enum": [
+    "summary",
+    "full"
+  ],
+  "type": "string"
+}
  2. Changed2 schema fields changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
    • removedInput schema / additionalProperties
      Removed value: -false
  3. Added

TDQS

A4.4/5.0
Behavior3/5

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

Rich context beyond annotations: 3 parallel internal API calls, no browser needed, results cached ~24h per URL, and MCP Apps hosts rendering an inline dashboard. Crucially it discloses the practical cost. Annotations already cover the safety profile but not these operational traits, so this adds real value.

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?

Long but front-loaded: synthesis purpose, weighting formula, when/when-not, cost, and return shape all appear in a deliberate order. Nearly every sentence carries load, though the return-shape enumeration and MCP Apps note push the length toward the upper bound of what is comfortable.

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?

With no output schema, the description fully carries the return contract: the top-level JSON keys, the composite scoring, the checks/synthesis arrays, and how the 'summary' variant differs by adding 'detail' and 'omitted'. An agent has everything needed to call it and interpret the response.

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 the baseline is 3. The description goes further, quantifying what each 'detail' value implies (75-100 KB / ~20k tokens for full vs ~4-31 KB for summary, plus the added 'detail' and 'omitted' fields), which the schema's terse enum description does not convey.

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?

States a specific verb and resource: 'Generate a unified design-intelligence report for a single URL, the synthesis capstone,' and immediately explains the mechanism (fires /score, /drift, /readiness in parallel and computes a weighted composite). It names the three sibling tools it supersedes, so an agent can distinguish it without opening any schema.

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?

Gives explicit when-to-use ('a single holistic assessment instead of three separate scans', 'when sharing a design-intelligence verdict') and an explicit 'When NOT to use' block routing to designesy_score, designesy_drift_score, and designesy_readiness_score. Nothing is left to inference.

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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