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NaniDAO

agentek-eth

by NaniDAO

predictTransferId

Generate a transfer ID for Ethereum token transfers by inputting sender, recipient, token ID, and amount. This tool helps verify transaction details before execution.

Instructions

Predict a transfer ID for a potential transfer

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fromYesThe sender address
toYesThe recipient address
idYesThe token ID
amountYesThe transfer amount
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'predict,' implying a read-only or simulation operation, but doesn't clarify if this is a safe, non-destructive call, what the output format might be (e.g., string, object), or any limitations (e.g., accuracy, rate limits). For a tool with no annotations, this leaves critical behavioral traits unspecified.

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 a single, efficient sentence ('Predict a transfer ID for a potential transfer') that directly states the tool's purpose without unnecessary words. It's front-loaded and easy to parse, though it could be slightly more informative (e.g., by hinting at use cases). Overall, it's concise and well-structured.

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

Completeness2/5

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

Given the complexity (predictive tool with 4 parameters), no annotations, and no output schema, the description is incomplete. It doesn't explain what a 'transfer ID' is, how the prediction works, or what the output entails (e.g., format, reliability). For a tool that likely involves simulation or estimation, more context is needed to understand its role and limitations.

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?

The input schema has 100% description coverage, with clear parameter definitions (from, to, id, amount). The description adds no additional meaning beyond the schema, such as explaining parameter interactions or constraints. Since schema coverage is high, the baseline score is 3, as the schema adequately documents parameters without needing extra detail from the description.

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

Purpose3/5

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

The description states the tool 'predicts a transfer ID for a potential transfer,' which clarifies the action (predict) and resource (transfer ID). However, it's somewhat vague about what a 'transfer ID' represents in this context (e.g., a transaction hash, reference number) and doesn't distinguish it from sibling tools like 'intentTransfer' or 'getTransaction,' which involve actual transfers or transaction data. It provides a basic purpose but lacks specificity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description offers no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., before executing a transfer), exclusions (e.g., not for actual transfers), or refer to sibling tools like 'intentTransfer' for real transfers or 'getTransaction' for existing IDs. Without such context, users must infer usage, which is insufficient for effective 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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