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cryptoguard_scan_token

Read-onlyIdempotent

Scan cryptocurrency tokens for anomalous market behavior using physics-based detection. Compares tokens to tier-matched peers to identify unusual metrics and assess risk levels.

Instructions

Scan a single token for anomalous market behavior using WaveGuard physics-based anomaly detection. Compares the token to TIER-MATCHED peers (microcaps vs microcaps, large-caps vs large-caps). Returns anomaly scores, risk level, and explanations.

Example: scan 'solana' to check if its metrics are unusual.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
coin_idYesCoinGecko coin ID (e.g., 'bitcoin', 'solana', 'pepe').
sensitivityNoAnomaly sensitivity multiplier (default: 1.0). Higher = more sensitive.
Behavior4/5

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

Annotations already indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds valuable context beyond annotations by explaining the tier-matching peer comparison method and the types of outputs (anomaly scores, risk level, explanations), which helps the agent understand the tool's operational behavior.

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, followed by key details and a practical example. Every sentence adds value without redundancy, making it efficient and easy to parse.

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 the tool's moderate complexity, rich annotations, and lack of output schema, the description is mostly complete. It explains the scanning method and output types but could benefit from more detail on result interpretation or error handling. However, it provides sufficient context for effective use.

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%, so the schema fully documents both parameters. The description does not add any additional meaning or syntax details beyond what the schema provides, such as elaborating on the 'sensitivity' multiplier's impact. Baseline 3 is appropriate when the schema handles parameter documentation.

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 ('Scan a single token for anomalous market behavior'), the method ('using WaveGuard physics-based anomaly detection'), and the comparison scope ('TIER-MATCHED peers'). It distinguishes this tool from siblings by focusing on anomaly detection rather than health checks, rug pulls, search, or trade validation.

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

Usage Guidelines3/5

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

The description implies usage by providing an example ('scan 'solana' to check if its metrics are unusual'), but it does not explicitly state when to use this tool versus alternatives like cryptoguard_rug_check or cryptoguard_validate_trade. No exclusions or prerequisites are mentioned.

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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