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

list_partitions

List a dataset's (coin, month) partitions the caller's plan can read. Defaults to a compact SUMMARY (coin/month counts, month range, totals) — pass summary=false to enumerate (paginated via limit/offset). Filter with coin and/or month. Use to choose a valid coin/month for read_dataset.

Input Schema

TableJSON Schema

Name	Required	Description
`coin`	No	Optional: only partitions for this coin, e.g. `BTC`.
`asset`	Yes	Dataset name, e.g. `gold_funding_1h`.
`limit`	No	Full mode only (`summary=false`): max partitions to return (clamped to 1000). Defaults to 200.
`month`	No	Optional: only partitions for this month, `YYYY-MM`.
`offset`	No	Full mode only (`summary=false`): partitions to skip, for pagination. Defaults to 0; pass the previous response's `next_offset` for the next page.
`summary`	No	Return compact coverage stats (coin/month counts, range, totals) instead of every `(coin, month)` row. Defaults to true — the full cross-product is hundreds of rows for some datasets. Set `false` to enumerate (paginated via `limit`/`offset`).

Output Schema

TableJSON Schema

Name	Required	Description
`note`	No	Set when the dataset exists but requires a plan the caller doesn't have.
`asset`	Yes
`summary`	No	Present in summary mode (the default): compact coverage stats.
`your_tier`	Yes
`partitions`	Yes	Present in full mode (`summary=false`): this page of partitions. Empty in summary mode.
`next_offset`	No	Full mode only: pass as `offset` to fetch the next page, or null when the listing is exhausted.
`generated_at`	Yes
`total_matching`	Yes	Full mode only: total partitions matching the filter, before pagination.

Schema Changelog

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

Changed4 schema fields changedJul 10, 2026

addedOutput schema / $defs / PartitionOut / properties / coverage

Added value: +{
+  "description": "Fraction of expected time-buckets populated — the tradability signal.\nPresent only for the time-series tiles (1m is gappy on thin coins, the\nhourly grids near-complete); absent for forecast/wallet datasets.",
+  "format": "double",
+  "nullable": true,
+  "type": "number"
+}

addedOutput schema / $defs / PartitionOut / properties / rows

Added value: +{
+  "description": "Parquet row count, when the manifest reports it (time-series tiles).",
+  "format": "int64",
+  "nullable": true,
+  "type": "integer"
+}

addedOutput schema / $defs / PartitionsSummary / properties / median_coverage

Added value: +{
+  "description": "Median populated-fraction across matched partitions that report coverage\n(time-series tiles only); null when the dataset carries no coverage.",
+  "format": "double",
+  "nullable": true,
+  "type": "number"
+}

addedOutput schema / $defs / PartitionsSummary / properties / min_coverage

Added value: +{
+  "description": "Lowest populated-fraction among matched partitions; null as above. A low\nvalue flags thin coins/months where naive rolling windows go wrong —\nswitch to `summary=false` to see per-partition coverage.",
+  "format": "double",
+  "nullable": true,
+  "type": "number"
+}

Changed3 schema fields changedJul 9, 2026
- addedOutput schema / $defs / PartitionOut / properties / coin / description
```
Added value: +"Coin symbol; absent for market-wide (coinless) datasets."
```
- addedOutput schema / $defs / PartitionOut / properties / coin / nullable
```
Added value: +true
```
- changedOutput schema / $defs / PartitionOut / required
```
Previous value: -[
-  "coin",
-  "month",
-  "size_bytes"
-]New value: +[
+  "month",
+  "size_bytes"
+]
```
First observedJul 6, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Since no annotations are provided, the description fully carries the behavioral burden. It discloses that partitions depend on the caller's plan, defaults to summary mode due to potentially large results, and explains pagination via limit/offset. It does not mention error conditions or rate limits, but covers the essential behavior well.

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 three sentences, each serving a distinct purpose: first states the core function, second details the output modes, third explains filtering and usage. No unnecessary words or redundancy.

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

Completeness4/5

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), the description adequately covers return values (summary stats vs. partition list) and pagination. It explains the two modes and filtering. It is slightly incomplete in not addressing edge cases like empty results or errors, but overall comprehensive.

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 baseline is 3. The description adds significant value beyond the schema by explaining the default behavior of summary (true), why summary is the default (hundreds of rows), and that limit/offset only apply in full mode. It clarifies the intent of coin and month filters.

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 lists (coin, month) partitions of a dataset that the caller's plan can read. It provides two modes (summary and enumeration) and explicitly mentions its use case: 'Use to choose a valid coin/month for read_dataset.' This differentiates it from sibling tools like list_datasets and read_dataset.

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?

The description gives clear context on when to use the tool, including filtering by coin/month and the two output modes. It states the primary use case ('choose a valid coin/month for read_dataset'). However, it does not explicitly mention when not to use it or compare with alternative tools.

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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