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Marcus-Rug-Intel

Rug Munch Intelligence

check_deployer_history

Analyze token deployer history to identify rug pulls and assess trustworthiness by reviewing deployed tokens, rug count, and classification.

Instructions

Check a token deployer's full history: tokens deployed, rug count, classification (legitimate_builder / suspicious / serial_rugger). Essential for evaluating new token trustworthiness. Cost: $0.06.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
deployer_addressYesDeployer wallet address
Behavior3/5

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

No annotations are provided, so the description carries full burden. It discloses cost ('Cost: $0.06'), which is useful behavioral context, but does not cover other traits like rate limits, authentication needs, response format, or error handling. The description adds some value but is incomplete for a tool with no annotations.

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 front-loaded with the core purpose in the first sentence, followed by usage context and cost. Every sentence earns its place with no wasted words, making it highly efficient and well-structured.

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, no output schema, and a simple input schema, the description is adequate but has gaps. It covers purpose, usage, and cost, but lacks details on return values, error cases, or behavioral constraints. It meets minimum viability but could be more complete for trust evaluation.

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 the single parameter 'deployer_address' documented in the schema. The description does not add meaning beyond the schema, such as format examples or constraints. Baseline 3 is appropriate when schema does the heavy lifting.

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 ('Check a token deployer's full history') and resource ('token deployer'), listing concrete outputs like tokens deployed, rug count, and classification. It distinguishes from siblings by focusing on deployer history rather than token risk, wallet checks, or other analytics.

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 usage ('Essential for evaluating new token trustworthiness'), but does not explicitly state when not to use it or name specific alternatives among siblings. It implies usage for trust assessment but lacks explicit exclusions or 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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