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amm_price_impact

Compute price impact and average price for trades on an AMM pool. Determine how large a trade the pool can absorb before quoting, using human-readable trade sizes.

Instructions

The depth / slippage table for one AMM pool: the realized average price and price impact for each of a list of trade sizes, exact to the wei. Use this to reason about how large a trade a pool can absorb before quoting. Sizes are human 'NUMBER SYMBOL' strings. Requires the optional flox-py dependency.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
poolYesA pool spec: {venue, token0:{symbol,decimals}, token1:{symbol,decimals}, ...venue params}.
sizesYesTrade sizes as 'NUMBER SYMBOL' strings.
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 the output is a table of average price and price impact, precise to the wei, and mentions a required dependency. However, it does not disclose read-only behavior, error conditions, or permissions.

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 two sentences plus a dependency note, with no wasted words. It front-loads the purpose and provides necessary input format details efficiently.

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, the description explains inputs (pool and sizes) and outputs (price impact table). It is adequate but could include examples or mention error cases for completeness.

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% for both parameters. The description adds clarity by specifying that sizes are 'NUMBER SYMBOL' strings and detailing the pool spec structure, going beyond the generic 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 states it provides a depth/slippage table for an AMM pool, listing realized average price and price impact for trade sizes. This is a specific verb and resource, and it clearly differentiates from sibling tools like price_amm_swap or compute_indicator.

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 advises using this tool to reason about how large a trade a pool can absorb before quoting. While it gives a clear usage context, it does not explicitly state when not to use it or list alternatives.

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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