get_learning_queue

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

No annotations are provided, so the description carries the full burden. It discloses the required auth scope, the governing constitutional policy, and the fact that every call is sealed with a ProofLink cryptographic receipt. This goes beyond typical descriptions, though it does not detail side effects (e.g., whether receipt generation affects state) or error/response behavior.

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: the first front-loads the core purpose and scope, the second adds essential context (auth and receipt). Every word earns its place with no fluff or repetition.

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 zero-parameter retrieval tool with no output schema, the description explains what it returns (patterns pending approval), the filter (Tier 2, score 50-85), and important contextual details (scope, policy, receipt). It is complete enough for an agent to select and invoke correctly, though it does not specify return format or pagination, which are not critical for basic usage.

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 tool has zero parameters, so the baseline is 4. The description adds no parameter detail (none exists), and the schema already covers everything (100% coverage). No further semantics needed.

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 uses a specific verb ('Get') and identifies the exact resource ('patterns pending human approval from Learning Arbiter') and scope ('Tier 2 items, score 50-85'). This clearly distinguishes it from sibling tools like approve_learning_item (which approves) and get_learning_metrics (which gets metrics).

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 provides clear context: it is for retrieving Tier 2 items (score 50-85) pending human approval, implying use when you need to review items in the approval queue. It also states a required scope (learning:arbiter:read), serving as a prerequisite. However, it does not explicitly mention when not to use it or name alternative tools for other queue types.

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