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talkincode

HyperLiquid MCP Server

by talkincode

get_orderbook

Fetch order book data for any trading pair, with configurable depth to analyze market depth, liquidity, and price levels.

Instructions

Get orderbook data for a specific coin

Args: coin: Trading pair (e.g., "BTC", "ETH") depth: Order book depth (default: 20)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
coinYes
depthNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only says 'Get orderbook data' without describing return format, pagination, rate limits, or any side effects. Given that the tool is a read operation, the lack of any behavioral context beyond the bare action is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise, with a clear first-sentence purpose and a compact Args list. Every sentence earns its place, and the structure is front-loaded with the primary purpose. It is efficient and easy to scan.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has a simple parameter set and an output schema exists, so the description does not need to explain return values. However, it lacks usage guidance and behavioral context, making it minimally sufficient for a straightforward read tool. Given the existence of overlapping sibling tools, it could be more complete by clarifying when to choose this tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema provides no descriptions (coverage 0%), so the description's Args section compensates by explaining both parameters: 'coin' as a trading pair with examples, and 'depth' as order book depth with a default value. This adds meaningful semantics beyond the raw schema, though it could be more detailed (e.g., depth units or allowed ranges).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Get orderbook data for a specific coin' with a specific resource (orderbook) and verb (get). It is distinct from siblings like get_market_data and get_candles_snapshot, but it does not explicitly differentiate itself or mention alternatives, so it lacks the explicit sibling differentiation seen in top-tier descriptions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool vs. alternatives. The description only lists arguments, with no mention of appropriate contexts, exclusions, or comparison to similar tools like get_market_data or get_candles_snapshot. This leaves the agent to guess which tool is appropriate for a given task.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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