rlm_get_results

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

No annotations provided, so description carries full burden. It only states 'retrieve', but does not disclose if operation is read-only, safety implications, or error handling (e.g., if name not found). Minimal transparency.

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 short sentences, no fluff. Front-loaded with action and resource. Could be clearer by mentioning output or usage scope, but no wasted words.

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?

Has output schema so return values are covered elsewhere, but description lacks usage guidelines and behavioral transparency. For a simple one-param tool, it's adequate but leaves gaps for an AI agent to understand when and how to use it effectively.

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?

Input schema has 0% description coverage. Description barely adds: 'name: Result set identifier' only repeats the parameter name with a generic label. No details on format, allowed values, or constraints beyond 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?

Description clearly states 'Retrieve stored results for aggregation', indicating action and resource. However, lacks explicit differentiation from siblings like rlm_get_chunk, and 'stored results' is somewhat vague.

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?

No guidance on when to use this tool versus alternatives like rlm_store_result or rlm_get_chunk. The description does not mention context, prerequisites, or exclusions.

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