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

describe_dataset

Get the full data dictionary for one dataset: prose plus every column's type, nullability and plain-English meaning. Use before read_dataset to choose columns.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`asset`	Yes	Dataset name, e.g. `gold_positioning_funding_factors_1d`.

Output Schema

TableJSON Schema

Name	Required	Description
`name`	Yes	Dataset name / asset key, e.g. `gold_ohlcv_1m`.
`note`	No	Optional "how to use this" callout.
`tier`	Yes	Display tier: `free` or `pro`. Re-derived from `policy.rs` on read, so it always matches actual entitlement regardless of the on-disk value.
`title`	Yes	Human-friendly title, e.g. "Order-flow OHLCV (1-minute)".
`cadence`	Yes	Granularity + partitioning, e.g. "1-minute bars, partitioned per (coin, month)".
`summary`	Yes	One-line intuitive summary — the catalog card.
`category`	Yes	Presentation category, e.g. `raw-tiles` or `forecast-layer`.
`keywords`	No	Per-dataset discovery keywords (schema.org keywords on the web).
`temporal`	No	Machine-readable timestamp/interval contract: what the label marks and how to join without leaking the future. Defaulted so snapshots predating the field still deserialize.
`use_case`	No	One-line "what you'd use it for" (buyer-intent) copy.
`seo_title`	No	Keyword-first SEO title tag (web `<title>`). Defaulted so older snapshots without the field still deserialize.
`description`	Yes	Longer prose — the dictionary page header.
`column_count`	Yes	Number of documented columns.
`column_groups`	Yes	Columns, grouped for presentation, in schema order.
`direct_answer`	No	40-60 word keyword-first lead answer — the definitional "what is this" blurb, and the strongest AI-citation extraction target. Defaulted for forward/backward compatibility with snapshots predating the field.

TDQS

A4.7/5.0

Behavior4/5

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

No annotations are provided, so the description carries the full burden. It accurately describes the output as a full data dictionary with prose and column details, indicating a read-only metadata operation. It does not mention any destructive behavior or limitations, but the context is sufficient for a simple metadata tool.

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 consists of two succinct sentences: the first explains purpose and output, the second provides usage guidance. Every word serves a purpose with no redundancy.

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 tool's simplicity (single parameter, output schema exists), the description covers all necessary aspects: what it does, what it returns, and when to use it. No gaps are apparent.

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% with a clear parameter description for 'asset'. The description adds value by linking the parameter to the tool's purpose (use before read_dataset) and explaining what the result contains, though it does not add new details about the parameter itself.

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 retrieves the full data dictionary for a dataset, specifying details like column type, nullability, and plain-English meaning. It distinguishes from sibling tools like read_dataset (which reads actual data) and list_datasets (which lists available datasets).

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 explicitly advises using this tool before read_dataset to choose columns, providing clear when-to-use guidance and implicitly contrasting with the alternative of directly reading data.

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

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources

Need Help?