rlm_index_recommendations

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

No annotations exist, so the description carries full burden. It only mentions returning a prioritized list of recommendations but does not disclose side effects, required permissions, data freshness, or how the recommendations are generated (e.g., real-time vs. cached).

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?

Two sentences, concise and front-loaded. Could be slightly improved by adding a one-liner on the output format, but overall 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?

Low complexity (no parameters, no output schema) but the description is adequate. It mentions the type of recommendations and priority but lacks specifics on the output structure (e.g., array of objects with fields). Given the tool's simplicity, this is satisfactory.

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?

There are no parameters, so the schema coverage is effectively 100%. The description does not need to add parameter details, and it appropriately describes the output without contradicting the schema.

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 returns actionable recommendations to improve index health, with specific examples like reindexing and coverage. It distinguishes from sibling tools like rlm_index_health and rlm_reindex by focusing on recommendations.

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 implies usage for index health improvement but does not explicitly state when to use this tool versus alternatives (e.g., after checking health with rlm_index_health). No exclusion criteria or when-not-to-use guidance is provided.

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