Hyperliquid Market Data — OHLCV, Funding Rates & Positioning (Tessera)

read_dataset

Read actual data rows from one (asset, coin, month) partition. Defaults to the latest 200 rows. Pass columns to limit width and limit (max 1000) to limit rows — a partition can be tens of thousands of rows. For a whole partition use get_download_url.

Input Schema

TableJSON Schema

Name	Required	Description
`coin`	No	Coin symbol, e.g. `BTC`. Omit for market-wide datasets (e.g. `gold_wallet_flow_1mo`) that have no coin dimension.
`asset`	Yes	Dataset name, e.g. `gold_positioning_funding_factors_1d`.
`limit`	No	Max rows to return (clamped to 1000). Defaults to the latest 200.
`month`	Yes	Partition month, `YYYY-MM`.
`order`	No	Which end of the partition to read. `latest` (default) returns the most recent rows — usually what you want for a "what's the current…" question.
`columns`	No	Columns to return. Strongly recommended — omitting returns every column, which is wide for some datasets. Use `describe_dataset` to see columns.

Output Schema

TableJSON Schema

Name	Required	Description
`coin`	No	Coin symbol; absent for market-wide datasets.
`note`	No	Guidance when the result was capped, or other advisories.
`rows`	Yes	One JSON object per row.
`asset`	Yes
`month`	Yes
`columns`	Yes	The columns actually returned, in order.
`row_count`	Yes
`truncated`	Yes	True when `total_rows_in_partition` exceeds the rows returned.
`total_rows_in_partition`	Yes	Total rows in the partition (before the row cap / limit).

TDQS

A4.7/5.0

Behavior4/5

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

With no annotations, the description carries the full burden. It discloses default row count, maximum limit, and that columns should be used to avoid wide results. It implicitly conveys read-only behavior. It could mention pagination or ordering behavior but is sufficient.

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?

Three sentences, no fluff. The main action is front-loaded. Every sentence adds value: first states purpose and default, second explains parameter usage, third points to alternative. Highly efficient.

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 presence of an output schema (not shown but signaled), the description does not need to explain return values. It covers defaults, limits, and when to use an alternative. For a read tool with 6 parameters and high schema coverage, this is complete.

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?

Schema coverage is 100%, baseline 3. The description adds meaning beyond schema: explains that limit defaults to 200 but can go up to 1000, that columns narrow the output, and that order defaults to latest. This helps an agent decide parameter values.

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?

The description clearly states the tool reads actual data rows from a specific partition identified by asset, coin, and month. It distinguishes itself from sibling tools like get_download_url (for whole partitions) and describe_dataset (for schema). The verb 'read' is specific and the resource scope is explicit.

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

Usage Guidelines5/5

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

The description provides explicit guidance: defaults to latest 200 rows, limit clamped to 1000, recommended to use columns to limit width, and explicitly directs to get_download_url for whole partitions. It does not leave ambiguity about when to use alternatives.

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

All Hyperliquid Market Data — OHLCV, Funding Rates & Positioning (Tessera) tools Inspect

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TDQS

A4.5/5.0

Disambiguation5/5

Each tool serves a distinct purpose: schema discovery (describe_dataset), bulk download (get_download_url), dataset listing (list_datasets), partition enumeration (list_partitions), cross-sectional ranking (query_cross_section), and row-level data access (read_dataset). No two tools overlap in functionality.

Naming Consistency5/5

All tool names follow a consistent snake_case verb_noun pattern (e.g., describe_dataset, list_partitions, read_dataset). The verbs are appropriately descriptive and the naming is uniform across the set.

Tool Count5/5

With 6 tools, the server is well-scoped for its purpose of providing market data access. The number is neither too few to be useful nor too many to manage, fitting comfortably within the optimal 3-15 range.

Completeness4/5

The tool set covers discovery, schema, reading, bulk download, and cross-sectional queries. A minor gap is the lack of a dedicated tool to retrieve a single coin's time series across multiple months without downloading entire partitions, but the existing tools still enable this workflow.

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