Get comprehensive research data for a token including grade, liquidity metrics, risk assessment, and percentile rankings.
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | Trading pair symbol | |
| exchange | Yes | Exchange ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavior. It indicates a read operation for research data but does not mention any potential destructive actions, rate limits, authentication requirements, or other side effects. The lack of transparency is a gap for a tool with no annotation support.
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 a single, short sentence that immediately conveys the tool's purpose. It is front-loaded with the key verb 'Get' and specifies the resource and data types. Every word is informative with no redundancy.
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 data retrieval tool with no output schema, the description provides a reasonable list of what the research data includes (grade, liquidity metrics, risk assessment, percentile rankings). This helps the agent understand the output's scope, though additional details about the response structure would improve completeness.
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%, with both parameters (symbol, exchange) having brief descriptions. The tool description does not add additional meaning or context beyond the schema, such as format constraints or examples. Baseline score of 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?
The description clearly states the tool retrieves comprehensive research data for a token, listing specific components like grade, liquidity metrics, risk assessment, and percentile rankings. This differentiates it from siblings like get_price or get_stats, though it could more precisely indicate that input is a trading pair (exchange + symbol), not a token address.
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?
No guidance on when to use this tool versus alternatives like get_price, get_stats, or get_token_intel. The description does not mention when not to use it or provide criteria for selection, leaving the agent to infer usage from the purpose alone.
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.