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, destructiveHint=false, fully covering the safety profile. The description adds context about the return format (percentages) and intended use, but does not disclose additional behavioral traits like data freshness or update frequency. With annotations carrying the main burden, the description provides adequate but not extra transparency.
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 long, front-loaded with the primary output definition and followed by usage guidance. Every sentence adds value, with no redundancy or fluff.
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?
Given no parameters, the presence of an output schema, and annotations covering safety, the description is complete. It explains what the tool returns, how to interpret it, and when to use it. No additional information is needed for correct invocation.
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 zero parameters and schema description coverage is 100%. The description does not need to explain parameter semantics. Baseline for 0 parameters is 4, and the description meets this without adding unnecessary information.
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 crypto market dominance metrics (BTC.D, ETH.D, USDT.D) as percentages of total market cap. This clearly distinguishes it from sibling tools like get_crypto_market (overall market data) and get_fear_greed (sentiment), which serve different purposes.
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 usage guidance: 'Use as a regime filter (risk-on/off) and a BTC vs altcoin rotation signal.' This tells the agent when to invoke this tool, though it does not explicitly exclude alternatives or list sibling tools for comparison. The context is clear enough for correct selection.
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.
Most tools target distinct domains (network, market, derivatives, macro, on-chain flows), and overlapping tools like get_derivatives vs get_derivatives_aggregate are clearly differentiated by scope and detail level. However, get_crypto_market and get_market_dominance both cover market-wide data, and get_btc_network and get_eth_whale_flows both touch on-chain activity, which could cause some confusion.
All tool names follow a consistent get_ prefix with descriptive nouns (e.g., get_btc_network, get_defi_overview, get_execution_cost). The pattern is uniform across all 16 tools, with no camelCase or inconsistent verb styles, making it very predictable for an agent.
With 16 tools, the server covers a broad but coherent domain of crypto market analysis—prices, on-chain, derivatives, macro, sentiment, and execution. Each tool addresses a distinct analytical need, and the count is well-scoped for a comprehensive agent, not excessive given the breadth of features.
The surface is remarkably complete for a market analysis agent, covering spot, derivatives, on-chain, macro, sentiment, history, and execution costs. Minor gaps exist: no direct tool for decentralized exchange (DEX) trading volumes or specific coin list discovery, and no tool for order book depth beyond the execution cost tool. However, agents can work around these with existing tools.