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check_honeypot

Simulate a sell order through Jupiter DEX to check if a Solana token is a honeypot. Zero cost, read-only operation with no transaction executed.

Instructions

Check if a Solana token is a honeypot by simulating a sell order through Jupiter DEX. Zero cost — only requests a quote, no actual transaction is executed. Returns a JSON object with isHoneypot (boolean) and sellability details. This is a read-only operation with no on-chain side effects or gas costs. Use this when you only need to verify sellability; use check_token_safety for a broader 8-layer analysis, or full_token_scan for the most comprehensive 10-layer scan including market data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
mintYesSolana token mint address
amountNoRaw token amount to simulate selling (default: 1000000)
Behavior5/5

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

Despite no annotations, the description fully explains behavior: read-only operation, no on-chain side effects, zero cost, only requests a quote. It also notes returns JSON with isHoneypot and sellability details, covering all behavioral aspects.

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 description is compact but includes necessary details; the second sentence is a bit long but contains relevant information about sibling tools. No superfluous content, and the main purpose is front-loaded.

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 the return type (JSON with isHoneypot and sellability details) but lacks specifics on sellability fields. For a simple check, it is adequate and covers cost, side effects, and usage context.

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?

Input schema has 100% coverage with clear descriptions for both parameters (mint and amount). The description adds context by explaining the simulation process, enhancing understanding of how parameters are used.

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 checks if a Solana token is a honeypot by simulating a sell order through Jupiter DEX, with specific verb and resource. It distinguishes from sibling tools like check_token_safety and full_token_scan, making the purpose unambiguous.

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 provides when to use this tool (verify sellability) and when to use alternatives (check_token_safety for broader analysis, full_token_scan for comprehensive scan). This gives clear guidance on tool selection.

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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