request_user_confirmation

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

With no annotations, the description carries the burden of behavioral disclosure. It reveals that confirmation must happen in AgentPay web or Telegram aid that chat 'ok' must not be treated as approval, setting a clear source-of-truth rule. However, it does not disclose whether the call is asynchronous, what it returns, or whether a pre-existing agent link is required.

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 compact and well-structured: it leads with the core action, follows with call conditions and concrete triggers, and ends with an essential warning. Every sentence earns its place, and there is no filler 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?

Given 0% schema coverage, no annotations, and no output schema, this description is incomplete. It omits parameter semantics, return behavior, and prerequisites (e.g., active AgentPay link via begin_agent_link). While purpose and usage are clear, an agent cannot fully determine how to construct a correct call from this definition alone.

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 explain the meaning or use of `amount`, `message`, or `purchase_id`. The tool's purpose implies that `message` is the confirmation prompt, but the parameter roles and required content are left to inference.

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 opens with a specific action and resource: 'Ask the human to confirm in AgentPay web or Telegram.' It then narrows the purpose by listing concrete approval contexts (spend, freeze, missing delivery data), which clearly distinguishes it from sibling tools like present_choices or get_choice_status.

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 explicitly states when to call the tool ('Call when the user must approve a spend, freeze, or missing delivery data'), provides concrete trigger phrases ('спроси меня', 'подтверди', HITL), and includes a critical when-not rule: chat 'ok' is not payment approval. This is actionable and unambiguous, even without naming an alternative tool.

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