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llm_approve_route

Approve or reject pending high-cost AI routing decisions, optionally downgrading to a cheaper model.

Instructions

Approve or reject a pending high-cost routing decision.

Use this when llm_route (or any routing tool) blocked a call because the estimated cost exceeded LLM_ROUTER_ESCALATE_ABOVE. The pending call is stored server-side until you approve or cancel it.

Args: approve: True to proceed with the call, False to cancel it. downgrade_to: Optional cheaper model to use instead of the blocked one (e.g. "gemini/gemini-2.5-flash" instead of "openai/o3").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
approveNo
downgrade_toNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description carries full burden. It states that the pending call is stored and that approve=True proceeds with the call. However, it does not detail potential side effects, permissions needed, or what happens after approval (e.g., billing implications).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with a clear two-paragraph structure, front-loaded with purpose. The arg list is well-organized. Minor improvement could be more structured formatting, but it is efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple two-parameter tool, the description covers the essential context: when to use, what the parameters do, and the state of the pending call. An output schema exists, so return values are assumed documented. No major gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description adds meaningful explanations for both parameters: approve (True to proceed, False to cancel) and downgrade_to (optional cheaper model with example). This adds significant value beyond the schema's titles and defaults.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: approve or reject a pending high-cost routing decision. The verb is specific (approve/reject) and the resource is well-defined. It distinguishes from sibling routing tools like llm_route and llm_reroute.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains when to use the tool: when a call is blocked due to cost exceeding a threshold. It mentions the pending call is stored server-side, implying approval/cancellation is required. However, it doesn't explicitly list alternatives or when not to use it.

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

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