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atmflow55

agentshield-mcp

by atmflow55

verify_contract

Audit smart contracts for security threats before transacting, detecting honeypots, rug pulls, high taxes, and hidden ownership.

Instructions

Verify a smart contract for security risks before transacting. Detects honeypots, rug pulls, high taxes, hidden ownership, and other threats. Costs $0.001 via x402 micropayment (USDC on Base or SOL). Always call this before approving any on-chain transaction.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chainNoChain ID: 1=Ethereum, 56=BSC, 137=Polygon, 8453=Base, 42161=Arbitrum, 10=Optimism, solana1
paymentNox402 payment proof. For Solana: JSON string {network:'solana',txSignature:'...'}. For EVM: {txHash:'0x...',network:'base-mainnet'}. Omit to get payment instructions.
contractYesContract address (0x... for EVM chains, base58 for Solana)
Behavior3/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It transparently mentions the cost ($0.001 via x402 micropayment) and the types of risks detected. However, it does not state whether the operation is read-only, what happens if payment is omitted (though the schema hints at it), or describe the return format/results structure, leaving some behavioral aspects undisclosed.

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, front-loaded with purpose, and includes a direct, actionable instruction. Every sentence adds value: purpose, threats detected, cost, and usage recommendation. No fluff or redundancy.

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

Completeness3/5

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

The description covers the core purpose, cost, and usage context. However, it does not differentiate from sibling tools like scan_contract and deep_scan, which may be confusing for an agent selecting among them. It also lacks information about the payment flow (e.g., how to provide payment or what happens if omitted) and expected return value, relying on the schema for some of this. Given the presence of closely related siblings, the description is not fully complete.

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%, with each parameter (chain, payment, contract) clearly described. The description adds no additional parameter semantics beyond what the schema already provides, so the baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Verify') and resource ('smart contract') with a clear purpose ('for security risks before transacting'). It also lists concrete threat types detected (honeypots, rug pulls, high taxes, hidden ownership). However, it does not differentiate from sibling tools like scan_contract or deep_scan, which likely perform similar contract security checks.

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 a clear usage directive: 'Always call this before approving any on-chain transaction.' This provides strong contextual guidance. However, it does not explicitly mention when not to use this tool or name alternatives (e.g., deep_scan for more thorough analysis), so it lacks exclusions and alternative comparisons.

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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