Get the actionable learning queue

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context by stating the inclusion of both ready and prerequisite-blocked nodes and the ordering by readiness and importance, but it doesn't explain how readiness is determined or how ordering is implemented in practice.

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 a single, front-loaded sentence that conveys the core behavior without filler. Every phrase contributes meaning: 'ready and prerequisite-blocked nodes' specifies scope, and 'ordered by readiness and importance' specifies ordering.

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?

For a tool with no output schema, no parameter descriptions, and only implied usage guidance, the definition is incomplete. It omits what the returned nodes look like, how goal_id filters results, and how to interpret prerequisite-blocked nodes. The two parameters are otherwise opaque to an agent.

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 description coverage is 0%, and the description does not compensate: neither 'limit' nor 'goal_id' is explained in words. An agent cannot infer from the definition what goal_id does (e.g., filter by goal) or how limit affects the returned queue, since the schema only provides type, default, and bounds.

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 a specific verb ('Return'), a specific resource ('learning queue'), and the exact content scope ('ready and prerequisite-blocked nodes, ordered by readiness and importance'). This distinguishes it from sibling tools like get_node_context or diagnose_node, which serve different purposes.

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?

Usage is only implied: the phrase 'actionable learning queue' suggests this is for retrieving nodes that are ready or blocked by prerequisites. However, there is no explicit when-to-use guidance, no stated condition for choosing this over alternatives like forest_overview or get_node_context, and no exclusion criteria.

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