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Agent Intel Mev Sandwich Risk Analyzer

agent_intel_mev_sandwich_risk_analyzer
Read-only

Theoretical sandwich-attack profit for a swap of a given size in one DEX pool, at the pool's current reserves (DexScreener) and the swap's slippage tolerance: the largest front-run that still lets the victim's trade succeed, and the profit it would give. Constant-product pools only, no mempool view, no competing bots: a worst-case estimate, not a prediction. Suggests a slippage tolerance. Price: $0.003 in USDC per successful call, paid with x402 (Base, Solana or Arc); failed calls are not charged.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pairYesPool (pair) address
sideNosell = victim gives base token; buy = victim gives quote token (default sell)
chainNoDexScreener chain name, e.g. base, ethereum (default base)
amountYesVictim's input amount in that token's units
feePctNoPool fee in percent, 0 to 5 (default 0.3)
slippagePctNoVictim's slippage tolerance in percent, 0 to 50 (default 1)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dexNo
noteNo
pairNo
riskNo
sideNo
chainNo
modelNo
sourceNo
checkedAtNo
victimAmountNo
victimMinOutNo
maxFrontRunSizeNo
theoreticalProfitNo
victimExpectedOutNo
victimSlippagePctNo
suggestedSlippagePctNo
victimPriceImpactPctNo
profitAsPctOfVictimSizeNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior5/5

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

Beyond the readOnlyHint/openWorldHint annotations, it discloses the data source (DexScreener current reserves), the model's key limitations (no mempool view, no competing bots, constant-product only, worst-case not predictive), and the billing model — $0.003 USDC per successful call via x402 on Base, Solana or Arc, with failed calls uncharged. Cost and failure-charging behavior are exactly the kind of context an agent needs before invoking a paid endpoint.

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?

The core purpose is front-loaded in the first clause, then caveats, then pricing — a sensible order with high information density and no filler. It is a single dense sentence chain, which makes it slightly harder to scan than if the limitations and pricing were split out, but every clause carries weight.

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?

With an output schema present, return values need no explanation, and the description covers inputs, data source, model assumptions and payment. The only residual gap is operational detail such as how stale the DexScreener reserves are and what happens if the target pool is not constant-product.

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

Parameters3/5

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

Schema description coverage is 100%, so every parameter including side, chain, feePct and slippagePct is already documented in the schema. The description reinforces the victim/slippage framing that explains why amount and slippagePct matter, but adds no format, default or unit guidance beyond what the schema already states; baseline 3 applies.

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 precise verb and resource: computes theoretical sandwich-attack profit for a swap of a given size in one DEX pool at current reserves. It bounds the scope explicitly (constant-product pools only, one pool, no mempool view, no competing bots), so an agent can tell it apart from generic slippage or MEV feeds without opening the schema.

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 clear applicability conditions — one constant-product pool, DexScreener reserves, victim trade with a given slippage tolerance — and frames the output as a worst-case estimate rather than a prediction, which tells the agent when to trust it. It never names a sibling though, notably not agent_intel_dex_depth_slippage_calculator, which is the obvious adjacent tool, so there is no explicit routing or when-not clause.

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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