get_orderbook
Use this when the user wants the live order book (bids/asks) for a Hyperliquid perp market.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| depth | No | Levels per side | |
| symbol | Yes | Coin symbol, e.g. BTC |
Use this when the user wants the live order book (bids/asks) for a Hyperliquid perp market.
| Name | Required | Description | Default |
|---|---|---|---|
| depth | No | Levels per side | |
| symbol | Yes | Coin symbol, e.g. BTC |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds the context that the data is 'live,' but does not disclose additional behavior such as pagination, rate limits, or response shape, so the value beyond annotations is modest.
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 a single, front-loaded sentence with no filler. It immediately states the invocation condition and the resource, earning every word.
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?
For a simple read-only tool with two parameters and a clear schema, the description is nearly sufficient. It could mention what the response contains (e.g., arrays of bids and asks at a given depth), but that is largely inferable from 'bids/asks' and the 'depth' parameter. Minor gap, not a significant one.
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%, so both parameters (symbol and depth) are already documented with types and constraints. The description does not add new parameter semantics beyond implying the order book contains bids/asks, which is already known from the tool name. 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 states a specific verb and resource: 'wants the live order book (bids/asks) for a Hyperliquid perp market.' This clearly identifies the tool's function and distinguishes it from siblings like get_price, get_markets, and get_candles by naming the unique data object.
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 an explicit trigger condition ('Use this when the user wants the live order book'), making when to use the tool clear. However, it does not mention alternatives or exclusion cases, so it stops short of full when/when-not guidance.
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.
Each tool targets a distinct resource or action, but get_markets and get_price overlap in the data they return (price, 24h change, volume, funding), differing mainly by all-markets vs single-coin scope. The descriptions are explicit enough that an agent should rarely misselect, though the boundary is slightly blurry.
All tool names follow a consistent verb_noun snake_case pattern: get_ for reads, plus clear action verbs like place_, preview_, cancel_, close_, and set_. There is no mixing of styles or vague generic verbs.
13 tools is well within the ideal range for a trading-focused server. Each tool covers a distinct part of the workflow without feeling bloated or redundant.
The tool surface covers the full trading lifecycle: market data, account/position/order/trade reads, order preview and placement, cancellation, position closing, and TP/SL management. No obvious dead-ends or missing core operations for the stated Hyperliquid perp trading purpose.