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simulate_health_factor

Simulate how a price change affects your AAVE health factor. Determine if a drop triggers liquidation.

Instructions

Use this when the user wants to simulate how a price change affects their AAVE health factor — 'What happens to my health factor if ETH drops 20%?', 'How much can WBTC fall before I get liquidated?', 'Simulate a 30% drop in my collateral asset'. Fetches the user's full position, computes current health factor, then recalculates it after applying the specified price change to the target asset. Health Factor < 1.0 means the position is liquidatable.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chainYesChain identifier
assetSymbolYesSymbol of the asset whose price changes (e.g. WETH, WBTC, USDC)
userAddressYesWallet address of the user (0x...)
priceChangePctYesPrice change percentage — negative for drops, positive for gains. E.g. -20 means the asset price falls 20%.
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It explains the internal steps (fetch position, compute health factor, apply price change) and defines the liquidatable threshold. However, it does not explicitly state that the operation is read-only or disclose authentication or rate limit requirements.

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 three sentences long, front-loaded with the use case, and each sentence serves a distinct purpose: usage, mechanism, and key definition. No redundant or irrelevant content.

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 no output schema and no annotations, the description covers the core functionality, example queries, and a critical threshold. It lacks detail on the response format or potential side effects, but overall provides sufficient context for correct tool invocation.

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%, but the description adds meaningful context by aligning parameters with the simulation scenario (e.g., assetSymbol as the target asset, priceChangePct with negative example). This goes beyond the schema's basic 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 the tool's purpose: simulating how a price change affects AAVE health factor. It provides specific example queries and explains the computation steps. This distinctly sets it apart from siblings, which are mostly data retrieval tools.

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 gives explicit when-to-use guidance with example user requests. However, it does not mention when not to use it or suggest alternative tools like get_aave_user_position for simply viewing current health factor, leaving some ambiguity.

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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