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.

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.

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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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