get_funding_ratesCurrent funding rates for Hyperliquid perpetuals. Positive rate = longs pay shorts (bearish bias); negative = shorts pay longs (bullish bias).
| Name | Required | Description | Default |
|---|---|---|---|
| coins | No | List of asset tickers to fetch, e.g. ["BTC", "ETH"]. Omit to fetch all available assets. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is clear. The description adds context about the sign interpretation (positive = bearish, negative = bullish) but does not disclose additional behavioral traits such as data freshness, rate limits, or pagination. The description adds some value but is not extensive beyond the 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?
The description consists of two concise sentences that clearly state the purpose and provide an interpretation of the data. There is no fluff or repetition; every sentence earns its place.
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's simplicity (one optional parameter, read-only, open world), the description is adequately complete. It explains the core purpose and the meaning of positive/negative rates. No output schema exists, so return value details are not required. The description could mention that it returns funding rates for all assets by default, but it's implied. Overall, sufficient 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 description coverage is 100%; the 'coins' parameter is fully described in the schema (list of asset tickers, omit for all). The description does not add any additional meaning or syntax details beyond what the schema already provides, so baseline 3 is appropriate.
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 current funding rates for Hyperliquid perpetuals, which is a specific verb+resource. It also explains the meaning of positive/negative rates, adding value. However, it does not explicitly differentiate from similar tools like get_top_funding_rates among many siblings, so it falls short of a 5.
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 getting current funding rates but does not provide explicit guidance on when to use this tool versus alternatives (e.g., get_top_funding_rates, get_funding_outliers). No when-not-to-use or prerequisite information is given.
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.