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Get Orderbook Depth

get_orderbook_depth
Read-only

Full orderbook depth + slippage estimate for any Hyperliquid perp or HIP-4 market. Returns top of book, spread, cumulative depth at $100/$500/$1k/$5k tiers, and estimated slippage for a given order size. Critical for HIP-4 farming and low-liquidity assets where market orders get destroyed by slippage.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
coinYesAsset ticker (BTC, ETH, SOL) or HIP-4 contract name (e.g. "BTC>81041@20260512-0600")
sideNoOrder side: "buy" (taker into asks) or "sell" (taker into bids)buy
size_usdcNoOrder size in USDC to estimate slippage for (default: 200)

TDQS

A4.5/5.0
Behavior5/5

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

Discloses return data beyond annotations: 'top of book, spread, cumulative depth at $100/$500/$1k/$5k tiers, and estimated slippage for a given order size'. Annotations already indicate read-only and open world, no contradiction.

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?

Two sentences, no wasted words, front-loaded with purpose. Efficient and clear.

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

Completeness5/5

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

Despite no output schema, description adequately describes return values (top of book, spread, depth tiers, slippage). Also covers use case and parameter linkage, sufficient for agent to select and invoke.

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

Parameters3/5

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

Schema coverage is 100%, so baseline 3. Description mentions 'given order size' linking to size_usdc parameter, but adds little beyond schema documentation.

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

Purpose5/5

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

Description clearly states 'Full orderbook depth + slippage estimate for any Hyperliquid perp or HIP-4 market', using specific verb and resource. Distinguishes from sibling 'get_orderbook' by adding depth tiers and slippage estimation.

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

Usage Guidelines4/5

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

Explicitly says 'Critical for HIP-4 farming and low-liquidity assets where market orders get destroyed by slippage', indicating when to use and providing context for avoiding it in other scenarios.

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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TDQS

A3.7/5.0
Disambiguation3/5

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.

Naming Consistency4/5

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.

Tool Count2/5

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.

Completeness5/5

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.