suggest_rules

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

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It states the tool analyzes and suggests but does not mention side effects, required permissions, whether it modifies memory, or any read-only nature. This lack of detail is a significant gap.

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?

The description is two sentences, front-loaded with the action and outcome, with no wasted words. It efficiently conveys the core purpose and usage context.

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?

Given no output schema and no annotations, the description is sparse. It does not explain the output format, how candidates are presented, or limitations such as the number of memories analyzed. For a suggestion tool, more detail is needed for complete understanding.

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?

The schema describes memory_type with enum and description, and max_candidates with default/min/max but no description. The tool description adds no additional meaning beyond 'multiple times' which is not tied to parameters. With 50% schema coverage, the description fails to compensate for gaps.

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 analyzes stored memories and suggests rule candidates based on detected patterns, distinguishing it from sibling tools like add_rule, list_rules, etc. The verb 'analyze and suggest' and resource 'memories/rule candidates' are specific and unambiguous.

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?

The description indicates usefulness for discovering repeated preferences that could become formal rules, giving context for when to use it. However, it does not explicitly state when not to use it or compare to alternatives like match_rules, leaving room for improvement in guidance.

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