get_top_funding_ratesTop Hyperliquid perps ranked by absolute funding rate, with OI and annualized yield. Useful for finding the most overcrowded longs/shorts and carry opportunities.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of top results to return (default: 10) | |
| min_abs_rate | No | Minimum absolute funding rate to include, e.g. 0.0001. Omit to include all. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, description reinforces safe read operation. Adds context about ranking by absolute funding rate and output fields, which is not in annotations.
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 sentences, no wasted words. Purpose and use case are 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?
No output schema, but description mentions included fields (OI and annualized yield). Lacks detailed output format, but acceptable for a list tool with simple parameters.
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 descriptions for both parameters. Description adds no new parameter details beyond schema, but baseline 3 is appropriate since schema is self-explanatory.
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?
Specifies verb 'get', resource 'top funding rates', and includes data fields (OI and annualized yield). Distinct from siblings like 'get_funding_rates' and 'get_funding_outliers' by focusing on top rankings by absolute rate.
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?
Explicitly states use case: finding overcrowded longs/shorts and carry opportunities. Does not contrast with sibling tools, but the context of being 'top' implies a specific selection criterion.
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.