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llm_approve_route

Approve or reject pending high-cost AI routing decisions. Optionally downgrade 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
Behavior4/5

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

The description explains that pending calls are stored server-side until approval or cancellation, and describes the effect of the 'approve' and 'downgrade_to' parameters. With no annotations, this offers good behavioral context, though the return value is not specified (output schema exists).

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

Conciseness5/5

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

The description is concise with a one-line summary, a usage paragraph, and an Args list. Every sentence adds value and is front-loaded.

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 tool with two parameters and an output schema, the description covers the scenario, parameters, and behavior adequately. A minor gap is clarifying the interaction when both approve and downgrade_to are provided, but overall it is complete enough.

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

Parameters4/5

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

The schema has 0% description coverage, but the description adds semantic meaning: 'approve: True to proceed with the call, False to cancel it' and 'downgrade_to: Optional cheaper model to use instead of the blocked one' with an example. This compensates for the lack of schema descriptions.

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 that the tool approves or rejects pending high-cost routing decisions. It specifies the trigger event (llm_route blocking due to cost exceeding threshold) and distinguishes from siblings like llm_reroute and llm_route.

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 explicitly says 'Use this when llm_route (or any routing tool) blocked a call because the estimated cost exceeded LLM_ROUTER_ESCALATE_ABOVE.' This provides clear context for when to use the tool, though it does not mention 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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