get_funding_outliersHyperliquid perps whose current funding rate deviates significantly from their 7-day average. A spike vs baseline is a stronger signal than raw rate.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Historical window in days to compute the baseline average (default: 7) | |
| min_deviation_factor | No | Minimum ratio of |current_rate| / |avg_rate| to qualify as outlier (default: 2x) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds a behavioral insight (spike vs baseline is stronger signal) beyond the annotations (readOnlyHint, openWorldHint). However, it does not disclose other traits like data freshness or pagination. Annotations already cover read-only safety.
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, front-loaded with the core function, followed by a valuable insight. No wasted 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 is adequate for a simple filtering tool but does not specify the return format or fields. Since there is no output schema, more detail on what is returned would be helpful.
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?
Both parameters are fully described in the input schema with clear descriptions and defaults. The description adds no further meaning, so baseline score of 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 identifies the resource (Hyperliquid perps with outlier funding rates) and the action (get those outliers). It also adds insight that a spike vs baseline is a stronger signal than raw rate. This distinguishes it from similar tools like 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 that raw funding rate is less useful but provides no explicit guidance on when to use this tool vs alternatives. No when-not or alternative tools are mentioned.
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.