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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, so the description needs only add value beyond those. It adds useful scoping ('this user's' open work) and alias semantics, but it does not disclose return format, pagination, or ordering behavior. This is acceptable but not rich.

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?

Three tight sentences each add something: scope and purpose, practical filter guidance, and alias clarification. There is no filler, repetition, or boilerplate.

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 simple listing tool with zero required parameters, the description is sufficiently self-contained: it explains who the result belongs to, why to use it, how to filter, and how it relates to claiming. No output schema exists, and the basic list contract is clear from the title and first sentence.

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 only 33%, but the description compensates for target_model and unclaimed_only by linking them to concrete work-discovery workflow: 'find work addressed to a model label' and 'before claiming a task'. It also reinforces the gpt/chatgpt aliases. The limit parameter is adequately explained by its schema constraints and name.

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 identifies a specific verb and resource: listing this user's open deliberations. It also explains the purpose — allowing an authenticated AI to discover work without being handed a UUID or canonical key — which clearly distinguishes it from sibling tools like get_deliberation and create_deliberation.

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 gives concrete usage context: use it for discovery without identifiers, and use target_model plus unclaimed_only to filter before claiming a task. It does not explicitly name sibling alternatives or say when not to use it, but the intended workflow is clear and actionable.

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