Live quotes for the S&P 500 (SPY ETF), gold (GLD ETF) and Nasdaq 100 (QQQ ETF). Macro context for risk-on/risk-off and the correlation of traditional markets with crypto. These are ETFs priced in USD per share, so use direction and percent change rather than the absolute index level.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| quotes | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds value by specifying the ETFs covered, the macro context, and the interpretation guidance (use direction/percent change). This goes beyond the annotations without contradicting them.
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 consists of three concise, front-loaded sentences. The first states what the tool provides, the second adds macro context, and the third gives practical usage guidance. Every sentence contributes essential information without 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?
Given the tool has no parameters and an output schema exists, the description adequately covers what the tool does and how to interpret its results. It names the specific ETFs and the correlation context, which is complete for the intended use. It could optionally list alternatives, but this is not essential.
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?
The tool has no parameters, and schema coverage is 100% trivially. Per rubric baseline for 0 parameters is 4. The description does not need to add parameter details, and it appropriately explains the meaning of the returned data (live quotes, USD per share, direction and percent change).
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 that the tool provides live quotes for three specific ETFs (SPY, GLD, QQQ) and macro context for risk assessment. It explicitly names the resources and distinguishes itself from crypto-focused tools by mentioning traditional markets and correlation with crypto.
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 implies usage for traditional market quotes and macro context relevant to crypto traders. It tells the user to focus on direction and percent change because the ETFs are priced in USD per share. However, it does not explicitly mention when not to use it or name direct alternatives like get_crypto_market.
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.
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.