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danteriva45-hub

DeFi Intelligence Engine

get_optimal_gas_window

Analyze historical gas patterns to find optimal transaction windows, reducing fees by 25-45%. Ideal for non-urgent transactions.

Instructions

Predict the best time window to execute transactions for minimum gas cost.

Uses historical Ethereum gas patterns (hourly + day-of-week multipliers) to identify upcoming low-gas windows. Typical savings: 25-45% vs peak hours.

Best windows: 3-7 AM UTC (daily low) and weekends (-20 to -25%).

Use this when: an agent can defer a non-urgent transaction to save on gas. Combine with get_gas_price for current baseline.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chainNoChain to optimize for: 'ethereum', 'arbitrum', 'base', 'polygon', 'optimism'ethereum
urgencyNoTransaction urgency: 'low' (can wait 24h), 'medium' (wait up to 6h), 'high' (execute now)low
horizon_hoursNoHours to look ahead for optimal windows (1-48). Default: 24

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations, the description reveals it uses historical gas patterns (hourly + day-of-week multipliers), typical savings (25-45%), and best windows (3-7 AM UTC, weekends). It doesn't mention data freshness or limitations, but adequately describes behavior for a prediction tool.

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?

Five efficient sentences: purpose, method + savings, best windows, usage guidance, and combination tip. Every sentence provides unique value, with no redundant or missing information.

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?

Given output schema exists (covers return values), the description adequately explains when to use (deferral), how it works (historical patterns), and context (savings, windows). It could mention other supported chains from schema, but overall sufficient for a prediction tool with optional parameters.

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?

Schema coverage is 100%, so baseline 3. The description adds value by explaining typical savings and best windows, which helps users interpret parameters like urgency and horizon_hours. However, it doesn't elaborate on the 'chain' parameter beyond mentioning Ethereum in examples.

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 predicts the best time window for minimum gas cost, with specific verb+resource. It implicitly distinguishes from sibling get_gas_price by saying 'Combine with get_gas_price for current baseline', making differentiation clear.

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

Usage Guidelines5/5

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

Provides explicit 'Use this when:' usage guidance: deferring a non-urgent transaction to save gas. Also suggests combining with get_gas_price for baseline, indicating alternative when immediate execution is needed.

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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