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get_spot_orderbook_history

Read-onlyIdempotent

Retrieve historical Hyperliquid spot L2 orderbook snapshots for a given coin pair over a specified time range, with configurable depth and pagination.

Instructions

Get historical Hyperliquid Spot L2 orderbook snapshots over a time range. Symbols are dashed canonical (e.g. 'HYPE-USDC'). Returns L2 snapshots with bids/asks. Live coverage from 2026-05-05 (no historical backfill before that date because Hyperliquid does not publish historical spot orderbook data). Build+ tier required for non-default depth.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
coinYesHyperliquid Spot dashed canonical pair symbol (e.g. 'HYPE-USDC', 'PURR-USDC'). 294 pairs available. The server resolves the dashed form to Hyperliquid's wire format ('PURR/USDC', '@107') internally. Use get_spot_pairs to list all.
startNoStart timestamp (Unix ms or ISO). Defaults to 24h ago.
endNoEnd timestamp (Unix ms or ISO). Defaults to now.
limitNoMax records to return (default 100, max 1000)
cursorNoPagination cursor from previous response's nextCursor
depthNoOrderbook depth — number of price levels per side

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
recordsYesArray of result records
countYesTotal number of records in the full result set
nextCursorNoCursor for next page, if more results available
Behavior4/5

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

Annotations already indicate read-only and idempotent. Description adds value by disclosing live coverage start date (2026-05-05, no backfill) and requiring Build+ tier for non-default depth. No contradiction with annotations.

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?

Four sentences, each conveying essential information without redundancy. Front-loaded with purpose, then details. No unnecessary words.

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?

Given the output schema exists, the description sufficiently covers purpose, parameters, constraints, and data availability. Mentions return type (bids/asks) and key behavioral notes. Complete for its complexity.

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 covers 100% of parameters with descriptions. Description adds moderate context for coin (dashed canonical, internal resolution) and clarifies start/end defaults. Does not significantly exceed schema information.

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 the tool gets historical Hyperliquid Spot L2 orderbook snapshots over a time range, specifying the resource and action. It differentiates from siblings by explicitly mentioning 'Spot' and 'L2', and contrasts with other orderbook tools in the sibling list.

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?

Description provides clear usage context: when historical snapshots are needed, symbol format, and tier requirement for non-default depth. It could explicitly state when not to use (e.g., for current orderbook use get_spot_orderbook), but the context is sufficient for an informed agent.

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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