Crypto market dominance: BTC.D, ETH.D and USDT.D as a percentage of total crypto market cap. Use as a regime filter (risk-on/off) and a BTC vs altcoin rotation signal.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| btcDominance | No | ||
| ethDominance | No | ||
| usdtDominance | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description does not need to repeat those. The description adds value by explaining the interpretative context (regime filter, rotation signal) beyond the annotations, though it does not disclose additional behavioral traits like rate limits or data freshness.
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 two sentences: first sentence states exactly what data is returned, second sentence gives usage guidance. Extremely concise, front-loaded with key information, and every sentence earns its place.
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?
The tool has 0 parameters and an output schema exists, so the description need not explain return values. It fully captures the tool's purpose and usage context (dominance percentages for regime filter/rotation signal). No gaps remain.
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?
There are 0 parameters, and schema coverage is 100% (trivially). The description does not need to explain parameters. The baseline for no parameters is 4, which is appropriate here.
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 explicitly states the tool returns 'BTC.D, ETH.D and USDT.D as a percentage of total crypto market cap', using specific verbs (get) and resources (market dominance values). It clearly distinguishes from sibling tools like get_fear_greed or get_market_quotes by specifying the exact data elements and their purpose.
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?
The description provides explicit guidance: 'Use as a regime filter (risk-on/off) and a BTC vs altcoin rotation signal.' This tells the agent exactly when to invoke this tool versus alternatives like get_market_history for price data or get_fear_greed for sentiment.
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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Each tool targets a distinct data domain—network health, market prices, news, DeFi, derivatives per-venue vs aggregate, economic calendar, ETH address, whale flows, execution cost, sentiment, implied volatility, macro rates, market brief, dominance, history, and traditional market quotes. The only similar pair (get_derivatives vs get_derivatives_aggregate) is clearly differentiated by level of detail, so there is no real ambiguity.
All tools follow the get_<domain> pattern with descriptive noun phrases (e.g., get_btc_network, get_eth_whale_flows, get_market_brief). No mixed verb styles or casing conventions appear, making the naming fully predictable and consistent.
At 17 tools, the surface is slightly above the ideal 3-15 range, but the breadth of the domain—spot, derivatives, on-chain, macro, sentiment, and execution—justifies each tool. It's borderline but each tool earns its place in a comprehensive market analysis agent.
The toolset covers nearly every major facet of crypto market analysis: prices, history, dominance, derivatives, on-chain activity, DeFi, macro, economic calendar, sentiment, implied volatility, execution cost, and a composite brief. Minor gaps like historical OHLCV or multi-chain on-chain analytics are not critical given the agent's stated focus.