get_whale_tradesRecent large trades on Hyperliquid perps above a notional threshold. Includes side (long/short), size, price, and timestamp.
| Name | Required | Description | Default |
|---|---|---|---|
| coin | Yes | Asset ticker to fetch whale trades for, e.g. "BTC", "ETH" | |
| min_notional_usdc | No | Minimum trade size in USDC to qualify as a whale trade (default: 50,000) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and openWorldHint=true. The description adds the data fields included (side, size, price, timestamp) and the threshold concept, but does not disclose potential behavioral traits like result ordering, pagination, or rate limits. This adds some value beyond annotations but remains limited.
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 concise sentences that directly state purpose and output fields. No extraneous information. Well-structured and front-loaded.
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 description covers the tool's purpose and key output fields, which is adequate given the simple resource and lack of output schema. However, it omits details like result ordering (e.g., most recent first), default limits, or pagination, leaving minor gaps for an agent.
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 coverage is 100% with clear parameter descriptions. The description provides overall context (Hyperliquid perps, threshold) but does not add significant meaning beyond the schema. It offers example values for 'coin' but no additional semantic detail for 'min_notional_usdc'.
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 recent large trades on Hyperliquid perps with a notional threshold. It specifies the resource and verb, and the mention of 'perps' and 'threshold' helps differentiate from sibling tools like get_whale_flow or get_whale_positions, though it does not explicitly compare.
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 no guidance on when to use this tool versus alternatives. It lacks context about scenarios, prerequisites, or exclusions, leaving the agent to infer usage from the name alone.
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.
Many tools are specialized, but several pairs have fuzzy boundaries: e.g., get_funding_rates vs get_top_funding_rates, get_basic_macro vs get_macro_context, get_simple_iv vs get_options_iv. An agent could easily select the wrong one.
Most tools follow a 'get_X' pattern with descriptive noun phrases. There are a few exceptions like 'create_api_key' and 'search_markets', but overall the convention is consistent and readable.
With 47 tools, the server is overloaded. While the domain is broad, this many tools makes discovery and selection difficult for an agent, reducing coherence.
The tool set covers an impressively wide range: macro data, funding, prediction markets, OI history, whale tracking, risk analytics, position sizing, backtesting, and signal generation. It leaves no obvious gaps for a crypto trading assistant.