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Designesy

by io.github.LE-VAI

designesy_llms_full_txt

Get the Designesy /llms-full.txt — the complete agent-facing brief: ingest protocol, discovery endpoints, every package, standing rules, anti-patterns, and a paste-ready agent prompt. Use this for comprehensive onboarding to the Designesy ecosystem when the short /llms.txt is not enough. When NOT to use: for a quick orientation, use designesy_llms_txt first (~500 tokens vs ~3000). Read-only — no side effects. Returns text/plain (~3000 tokens, includes a paste-ready agent prompt). No parameters.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.9/5.0
Behavior5/5

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

No annotations are provided, so the description carries full burden. It discloses read-only status with 'no side effects', describes return format as 'text/plain', and mentions estimated token count and included paste-ready prompt. This fully covers behavioral expectations for a zero-parameter read tool.

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 description is compact yet information-dense: purpose, contents, usage guidance, alternative, safety note, return type, and token estimate all fit in three sentences. It is front-loaded with the primary action and never wastes words.

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?

Despite having no output schema, the description explicitly states the return type and content details. It also provides usage context distinguishing it from the sibling tool. For a simple read-only fetch with no parameters, this is fully complete.

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?

The tool has zero parameters, which is already visible in the schema. The description adds 'No parameters' for confirmatory clarity. Per calibration, a baseline of 4 is appropriate for zero-parameter tools, and no further semantic explanation is needed.

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 clearly states the tool gets the Designesy /llms-full.txt, described as the complete agent-facing brief. It explicitly distinguishes from the sibling tool designesy_llms_txt by noting the short version is for quick orientation.

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?

Provides explicit when-to-use guidance: 'when the short /llms.txt is not enough' for comprehensive onboarding. Also gives a direct when-not-to-use with an alternative tool and token comparison (~500 tokens vs ~3000), making the decision context very clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Designesy toolsInspect
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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.

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