get_liquidation_clustersEstimated price levels where mass liquidations concentrate for a given Hyperliquid perp, computed from mark price and standard leverage multiples. Higher nearby orderbook liquidity = stronger support/resistance.
| Name | Required | Description | Default |
|---|---|---|---|
| coin | Yes | Asset ticker to analyze, e.g. "BTC", "ETH", "SOL" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint. The description adds that the tool computes estimates from mark price and standard leverage multiples, and mentions liquidity significance. It does not disclose additional traits like accuracy, rate limits, or response format beyond the estimation nature.
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, front-loaded with the core purpose, and every sentence adds value. No unnecessary words.
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 explains what the tool computes and hints at interpretation through liquidity context. However, without an output schema, it would benefit from specifying the return format (e.g., list of price levels). Still, for a simple tool with one clear parameter, it is reasonably complete.
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 input schema covers parameter 'coin' with description and examples. The tool description adds no additional meaning beyond the schema, which is sufficient for the single parameter. Baseline 3 applies.
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 estimates liquidation price levels for a given Hyperliquid perp, using mark price and leverage multiples. It specifies the resource and output, distinguishing it from sibling tools like get_orderbook or get_funding_rates.
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 analyzing liquidation clusters but does not provide explicit guidance on when to use vs. alternatives or when not to use. No context about prerequisites or exclusions.
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.