Search for a token's CoinGecko coin ID by name, symbol, or contract address. Use this first if you're unsure of the correct coin_id for scan_token or validate_trade.
Example: search 'pepe' to find the correct coin ID.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Token name, symbol, or contract address to search. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, covering the safety profile. The description adds useful behavioral context about returning CoinGecko coin IDs and demonstrates usage with an example, going beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise, with purpose, usage guidance, and an example in just two sentences. No redundant information; every sentence serves a clear function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple search tool with a single parameter and no output schema, the description is fully adequate. It covers purpose, usage context, and example, while annotations cover safety. It's complete for an agent to select and invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the parameter is already well-documented. The description adds marginal value by clarifying the search purpose and providing an example, but doesn't uniquely enrich the parameter semantics beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: searching for a token's CoinGecko coin ID by name, symbol, or contract address. It also distinguishes itself from sibling tools by explicitly referencing scan_token and validate_trade as tools that require this coin_id.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit when-to-use guidance: 'Use this first if you're unsure of the correct coin_id for scan_token or validate_trade.' This names alternatives and gives clear usage context, plus an example for added clarity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tools overlap in purpose: validate_trade and validate_trade_plus both validate trades, while scan_token and rug_check are bundled into validate_trade's checks. This creates ambiguity about which tool to use for a given task, especially for agents trying to distinguish the premium versus standard validation flow.
All tool names share the 'cryptoguard_' prefix and snake_case, but the structure varies: some are verbs (search, scan_token, validate_trade), while others are nouns or adjective-noun combinations (health, rug_check, counterfactual_trade). The inconsistency in verb usage makes the set feel less predictable, though the prefix provides a unifying element.
Seven tools is within the typical well-scoped range for a crypto risk assessment server. The count is reasonable, but the overlap between validate_trade and validate_trade_plus suggests one could arguably be consolidated, making the set slightly redundant.
The server covers the core domain well: health checking, token search, rug pull assessment, anomaly scanning, trade validation, and counterfactual analysis. Minor gaps exist (e.g., no batch processing or explicit market data endpoints) but the main workflows for trade validation and risk assessment are represented.