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 (coinless) datasets that have no coin dimension — none are currently published.
`asset`	Yes	Dataset name, e.g. `gold_ohlcv_1m`.
`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).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

Changed2 schema fields changedSep 3, 2026

changedInput schema / properties / asset / description

Previous value: -"Dataset name, e.g. `gold_positioning_funding_factors_1d`."New value: +"Dataset name, e.g. `gold_ohlcv_1m`."

changedInput schema / properties / coin / description

Previous value: -"Coin symbol, e.g. `BTC`. Omit for market-wide datasets (e.g.\n`gold_wallet_flow_1mo`) that have no coin dimension."New value: +"Coin symbol, e.g. `BTC`. Omit for market-wide (coinless) datasets that\nhave no coin dimension — none are currently published."

Changed7 schema fields changedJul 9, 2026

addedInput schema / properties / coin / default
```
Added value: +null
```

changedInput schema / properties / coin / description

Previous value: -"Coin symbol, e.g. `BTC`."New value: +"Coin symbol, e.g. `BTC`. Omit for market-wide datasets (e.g.\n`gold_wallet_flow_1mo`) that have no coin dimension."

addedInput schema / properties / coin / nullable
```
Added value: +true
```

changedInput schema / required

Previous value: -[
-  "asset",
-  "coin",
-  "month"
-]New value: +[
+  "asset",
+  "month"
+]

addedOutput schema / properties / coin / description

Added value: +"Coin symbol; absent for market-wide datasets."

addedOutput schema / properties / coin / nullable
```
Added value: +true
```

changedOutput schema / required

Previous value: -[
-  "asset",
-  "coin",
-  "month",
-  "columns",
-  "row_count",
-  "total_rows_in_partition",
-  "truncated",
-  "rows"
-]New value: +[
+  "asset",
+  "month",
+  "columns",
+  "row_count",
+  "total_rows_in_partition",
+  "truncated",
+  "rows"
+]

First observedJul 6, 2026

TDQS

A4.7/5.0

Behavior4/5

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

No annotations are present, so the description carries the disclosure burden. It adds real behavioral context: default of latest 200 rows, a 1000-row cap, and the scale warning that a partition can be tens of thousands of rows. It stops short of describing empty-result or error behavior, but the output schema covers the return shape.

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, front-loaded with the core verb+resource, then defaults and bounds, then the sibling route. Every sentence earns its place; no filler or repetition of the schema.

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?

For a read tool with two required params, 100% schema coverage, and an output schema, the description covers everything an agent needs to invoke correctly: what it reads, defaults, bounds, and when to switch to get_download_url. Return values are already covered by the output schema.

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%, so the baseline is 3. The description adds value above baseline by explaining why limit matters (partition scale), the default row count, and that omitting columns returns a wide result — reinforcing the columns and limit parameters with rationale.

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?

States a specific verb and resource: 'Read actual data rows from one (asset, coin, month) partition.' This clearly differentiates it from siblings — describe_dataset (metadata), list_datasets/list_partitions (enumeration), and get_download_url (whole-file download), which is explicitly named.

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?

Contains an explicit routing rule: 'For a whole partition use get_download_url.' It also provides invocation guidance — 'Pass columns to limit width and limit (max 1000) to limit rows — a partition can be tens of thousands of rows' — so an agent knows how to size requests correctly.

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 clearly distinct role in the data access workflow: discovering datasets, understanding schema, listing partitions, reading sample rows, and downloading full partitions. There is no meaningful overlap, and any potential overlap between read_dataset and get_download_url is explicitly disambiguated by row-cap versus full-bulk access.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern using snake_case: describe_dataset, list_datasets, list_partitions, read_dataset, get_download_url. The pattern is predictable and makes the purpose of each tool immediately understandable.

Tool Count5/5

Five tools is a well-scoped size for a market data access server. Each tool covers a necessary step in the user journey without redundancy or unnecessary surface area.

Completeness5/5

The tool set forms a complete workflow: discover available datasets, understand their schema, inspect available partitions, read bounded samples, and download full partitions for bulk access. There are no obvious gaps or dead ends for the stated market data purpose.

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