Recent early-warning signals for a token (last 7 days): warning type, severity (1-5), multi-layer convergence score, outcome (confirmed/false_alarm/pending). Higher convergence = more independent layers agree something is wrong.
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | Trading pair symbol (e.g. BTC/USDT) | |
| exchange | Yes | Exchange ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses return fields, severity scale, convergence meaning, and outcome states. With no annotations, description provides sufficient read-only behavioral context, though authentication or rate limits are omitted.
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?
Two sentences, front-loaded with key information. No fluff; every word adds value.
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?
Covers time range, return fields, and convergence interpretation. Lacks explicit mention of result format (list of objects) but overall sufficient for a simple query tool with two parameters.
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 covers 100% of parameter descriptions (symbol and exchange). Tool description adds no extra semantic value beyond the schema's text, so baseline 3 is appropriate.
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?
Clearly states the tool retrieves early-warning signals for a token over the last 7 days, specifying return fields and interpretation. Distinguishes from sibling tools like get_price or get_microburst as it focuses on warnings.
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?
Implied usage for monitoring risk or anomalies, but no explicit comparison to siblings or when not to use. Lacks guidance on prerequisites or alternatives.
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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Most tools have distinct purposes, but get_token_intel, get_token_research, and get_microburst overlap in coverage of token intelligence, which could cause agent misselection. Descriptions are detailed but some redundancy exists.
Tools follow a consistent verb_noun pattern (agent_*, get_*, search_tokens). Minor deviations like 'get_microburst' and 'get_quartermaster' use less conventional nouns, but overall pattern is clear.
With 15 tools, the count is well-scoped for an intelligence platform covering agent interaction, token data, trading insights, and cross-venue analysis. Each tool serves a distinct purpose without being overwhelming.
The set covers identity management, fundamental data, price/market stats, orderbook microstructure, cross-venue verification, and comprehensive token intelligence. Minor gaps like historical data or advanced analytics are omitted, but core workflows are well-supported.