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Aayat AI

Trade precheck ($0.03)

trade-precheck
Read-only

Live pre-trade check before buying or selling a token: go/caution/no-go with reasons, from token safety and taxes, liquidity and estimated price impact for your trade size, holder concentration, the chain's swap fee now and the round-trip cost. 8 chains incl. Base, Solana, Ethereum. address: ..., chain: base, amountUsd: 1000, side: buy. Cheaper: /trade/precheck/cached. With AI: /trade/precheck/deep. Price: $0.03 in USDC per call (x402 or prepaid credits). In the free trial.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sideNobuy or sell.buy
chainNoBlockchain: base, solana, ethereum, bsc, arbitrum, polygon, optimism, avalanche.base
addressYesToken contract address (0x...) or Solana mint address.
amountUsdNoTrade size in USD (for price impact and fee share).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
gasNoCurrent swap fee on the chain in USD and as % of the trade.
tierYes
chainYes
costsYesEstimated round-trip cost %.
tokenNo
tradeNo
marketNo
safetyYesVerdict, score, danger and warning codes, buy/sell tax %.
addressYes
holdersNo
reasonsYesBlockers first, then cautions.
sourcesNo
decisionYes
checkedAtNo
liquidityYesTotal and main-pool USD, locked %, estimated price impact for this trade, largest trade for 1% impact.
disclaimerNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint/openWorldHint, but the description adds material context beyond them: concrete output form (go/caution/no-go with reasons), 8-chain coverage, the cost model ($0.03 USDC per call via x402 or prepaid credits), and trial availability. Pricing and payment/auth requirements are exactly the kind of behavioral detail structured fields don't carry.

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?

Core purpose and the 'before buying or selling' framing are front-loaded, and every clause (features, example, alternatives, pricing) carries information. It is dense and slightly run-on with several truncated fragments, keeping it out of the top band.

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

Completeness5/5

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

With an output schema present, the description needn't explain returns, yet it still summarizes the verdict shape. Combined with chain coverage, pricing, and alternatives, an agent has everything needed to call it correctly.

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% with enums for side and chain, so the schema already does the heavy lifting (baseline 3). The description adds value with a concrete invocation example ('address: ..., chain: base, amountUsd: 1000, side: buy'), which clarifies typical argument shape beyond the field 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?

States a specific verb and resource ('Live pre-trade check before buying or selling a token') and enumerates exactly what the verdict covers: safety, taxes, liquidity, price impact, holder concentration, and swap fees. It also names its two siblings (cached, deep) so an agent can disambiguate without opening any schema.

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?

Explicitly routes the agent to alternatives with selection conditions: '/trade/precheck/cached' when cheaper, '/trade/precheck/deep' when AI is wanted. It also notes the free-trial availability, giving a clear usage context.

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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