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Marcus-Rug-Intel

Rug Munch Intelligence

check_batch_risk

Analyze up to 20 cryptocurrency tokens simultaneously to detect potential scams, rug pulls, and honeypots for portfolio risk assessment.

Instructions

Batch risk check for up to 20 tokens at once. Returns risk scores and recommendations for all tokens. Ideal for portfolio screening. Cost: $0.30 (~$0.015 per token).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tokensYesList of token addresses (max 20)
chainNosolana
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It usefully adds cost information ('Cost: $0.30 (~$0.015 per token)') and throughput constraints ('up to 20 tokens at once'), which are valuable behavioral traits. However, it doesn't mention rate limits, authentication requirements, error handling, or response format details that would be helpful for a paid API tool.

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 perfectly concise with three tightly packed sentences that each earn their place: first establishes core functionality, second specifies output, third provides cost and use case. No wasted words, and key information is front-loaded.

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?

Given no annotations and no output schema, the description does well on purpose and cost transparency but leaves gaps. It doesn't describe the return format (what 'risk scores and recommendations' look like), error conditions, or authentication needs. For a paid batch processing tool with 2 parameters (one poorly documented), this is minimally adequate but incomplete.

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 50% (only 'tokens' parameter has a description). The description adds no specific parameter semantics beyond what's implied by 'tokens' and 'batch risk check.' It doesn't explain the 'chain' parameter's purpose or valid values, nor does it provide format details for token addresses. With partial schema coverage, the description doesn't adequately compensate for the undocumented 'chain' parameter.

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 specific action ('batch risk check'), resource ('tokens'), scope ('up to 20 tokens at once'), and output ('risk scores and recommendations for all tokens'). It distinguishes from siblings like check_token_risk (single token) and check_blacklist (different risk dimension) by emphasizing batch processing and portfolio screening context.

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 provides clear context for when to use this tool ('ideal for portfolio screening'), implying it's for bulk analysis rather than single-token checks. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools, though the 'batch' nature suggests it's for efficiency with multiple tokens.

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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