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Get Rule Guidance

get_rule
Read-onlyIdempotent

Returns complete guidance for one known rule slug: requirements, rationale, verification and remediation prompts, code examples, sources, and related rules. Use it when the user requests detailed guidance for a specific frontend rule. Unknown slugs return similar suggestions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesRule slug: the kebab-case rule ID, e.g. 'alt-text' or 'doctype'. The ID must identify a rule in the published catalogue.
includeUrlNoInclude the rule's web URL in response (default: false)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNo
slugNo
titleNo
contentNo
messageNo
promptsNo
sourcesNo
priorityNo
aiContextNo
categoriesNo
difficultyNo
descriptionNo
subcategoryNo
suggestionsNo
relatedRulesNo
estimatedTimeNo
sourceSummaryNo
primaryCategoryNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / slug / description
      Previous value: -"The rule's unique slug (e.g., 'doctype', 'alt-text')"New value: +"Rule slug: the kebab-case rule ID, e.g. 'alt-text' or 'doctype'. The ID must identify a rule in the published catalogue."
  2. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, non-destructive and closed-world, so the safety profile is covered. The description adds genuine behavioral context beyond that: unknown slugs do not fail but return similar suggestions, and it enumerates the response payload's content areas. It says nothing about rate limits or response size, but for a read-only lookup that gap is minor.

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?

Two sentences, front-loaded with the payload contents and followed by the usage condition. The first sentence's long enumeration is justified because it is the main differentiator from lookup/validation siblings, though it is list-heavy and could be tightened.

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

Completeness4/5

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

With an output schema present, the description need not explain return values in detail, and it correctly focuses on scope, usage condition and the unknown-slug fallback. The one missing element is explicit routing among the many rule-related siblings, which matters given ten peer tools.

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?

Schema coverage is 100% and both parameters are documented there, including the kebab-case slug format and the includeUrl default, so the schema does the heavy lifting. The description only reinforces 'one known rule slug' and the unknown-slug fallback, adding marginal semantics. Baseline 3 applies when the schema is complete.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource ('Returns complete guidance for one known rule slug') and enumerates exactly what is returned: requirements, rationale, verification/remediation prompts, code examples, sources, related rules. It is clearly not a discovery or validation tool. However, it never differentiates itself from close siblings like explain_rule or check_rule, so the agent must infer the boundary.

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?

'Use it when the user requests detailed guidance for a specific frontend rule' gives a clear triggering condition and implicitly covers the 'known slug' prerequisite. It offers no when-not guidance and does not route the agent to search_rules (for discovery) or explain_rule (for a lighter explanation), leaving sibling disambiguation 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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