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

get_download_url

Return a short-lived presigned URL to download the full parquet for one (asset, coin, month) partition. Use for bulk access beyond read_dataset's row cap.

Input Schema

TableJSON Schema

Name	Required	Description
`coin`	No	Coin symbol, e.g. `BTC`. Omit for market-wide datasets that have no coin dimension.
`asset`	Yes
`month`	Yes	Partition month, `YYYY-MM`.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`url`	Yes	Presigned Tigris URL for the full parquet partition.
`expires_at`	Yes	RFC3339 expiry.

Schema Changelog

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

Changed4 schema fields changedJul 9, 2026

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

addedInput schema / properties / coin / description

Added value: +"Coin symbol, e.g. `BTC`. Omit for market-wide datasets that have no coin\ndimension."

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

changedInput schema / required

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

First observedJul 6, 2026

TDQS

A4.4/5.0

Behavior4/5

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

With no annotations, description adds behavioral context: 'short-lived presigned URL' indicates temporal and access properties. It doesn't detail error handling or authentication, but the core behavior is clear.

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?

Two concise sentences: first defines purpose and output, second gives usage guidance. No wasted words, information is front-loaded.

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?

Tool is simple with 3 params and output schema; description covers purpose, usage context, and parameter grouping. Lacks details like expiration duration or error cases, but sufficient for a URL retrieval tool.

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?

Description groups parameters (asset, coin, month) into a partition concept, adding meaning beyond schema. It also notes that coin can be omitted for market-wide datasets, providing practical guidance.

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?

Description clearly states the specific verb (return) and resource (short-lived presigned URL for a partition), and distinguishes from siblings like read_dataset by specifying its use case for bulk access.

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?

Explicitly states when to use this tool ('Use for bulk access beyond read_dataset's row cap'), providing clear context and an alternative tool. No exclusions mentioned.

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.

Resources

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