get_inferred_rules

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

Discloses read-only nature and no side effects. Explains output format (JSON with list of rules or error if no rules generated). Lists valid category filters. Comprehensive behavioral disclosure without annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Four sentences, each serving a purpose. First sentence is the verb+resource statement. No redundant or excessive text. Highly efficient.

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

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

Covers purpose, output, error handling, valid parameter values, usage context, and sibling pointers. No gaps given the tool's simplicity and lack of output schema.

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

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

Schema coverage is 0% but description adds all necessary semantics: lists valid values for category (spacing, color, typography, etc.) and explains behavior with empty string. Fully compensates for lack of schema description.

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

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

The description clearly states the tool retrieves design rules inferred from codebase patterns, and specifies the returned data (list of rules with category, pattern, confidence). It distinguishes from siblings by naming alternative tools for related tasks.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use: 'Use this to understand implicit conventions the codebase follows.' Also provides clear alternatives: 'For explicit design token values, use get_token. For source conflicts, use get_conflicts.' No ambiguity.

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