Tessera Analytics - Hyperliquid Market Intelligence

get_universe

The coverage picture: the last complete day, how many markets are in the universe for a scope, how much history sits behind them, and how densely it is populated. A compact SUMMARY by default — pass top_n (1..50) only if you need the per-market day counts.

Input Schema

TableJSON Schema

Name	Required	Description
`day`	No	The day to describe, `YYYY-MM-DD` or `latest`.
`scope`	No	`crypto` (the default), `hip3`, or `all`.
`top_n`	No	Per-market detail is off by default; passing a value (1..50) returns that many markets, listed by name, alongside the summary.

Output Schema

TableJSON Schema

Name	Required	Description
`as_of`	Yes	The day the per-population counts describe.
`coins`	No	Per-market detail, only when requested. Markets are listed by name.
`notes`	Yes
`scope`	Yes
`source`	Yes
`min_day`	Yes	The oldest day the counts below cover: the published range start, or the caller's history floor when the plan clips it (the response says so in `notes`).
`day_count`	Yes	Distinct days in the range.
`row_count`	Yes	Rows in scope over the whole range.
`truncated`	Yes	True when more markets exist than the detail list returned.
`months_covered`	Yes
`coin_count_hip3`	Yes	Markets with a deployer prefix on the described day.
`coin_count_total`	Yes	Distinct markets in scope over the whole range.
`max_complete_day`	Yes	The newest complete day — the day `latest` resolves to.
`coin_count_crypto`	Yes	Markets without a deployer prefix on the described day, whatever scope was asked for.
`rows_per_market_day`	Yes	`row_count` divided by (days × markets) — how densely the range is populated. Below 1.0 means some markets are missing some days.
`coverage_from_manifest`	Yes	True when the day range came from the published coverage manifest, false when it had to be read from the data itself.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 21, 2026

changedOutput schema / properties / min_day / description

Previous value: -"The oldest day available."New value: +"The oldest day the counts below cover: the published range start, or the\ncaller's history floor when the plan clips it (the response says so in\n`notes`)."

AddedSep 15, 2026

TDQS

B3.4/5.0

Behavior3/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It discloses that the default output is a compact SUMMARY and that top_n (1..50) returns per-market day counts, which is useful. However, it doesn't mention error handling, invalid inputs, rate limits, or authentication requirements, and it's vague about what 'history behind them' and 'densely populated' mean in output terms.

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 sentences with no redundancy. The core purpose is front-loaded ('coverage picture'), the default behavior is stated, and the optional parameter is introduced clearly. Every word earns its place.

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?

An output schema exists, so return values are covered structurally. The description is sufficient for an agent to understand what the tool does and how to get the summary or per-market detail. Minor gaps like examples or typical use cases would elevate it further, but it's adequate given the schema coverage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description adds a little value by explaining top_n's purpose ('per-market day counts') and its valid range (1..50), which the schema also states but in a more terse way. Day and scope are not elaborated beyond the schema, so the description doesn't materially enhance understanding of those parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool provides a coverage picture: last complete day, market count, history depth, and density. It uses a specific verb ('get' implied) and resource ('universe'), and the output type (SUMMARY vs per-market detail) is explicit. It doesn't name sibling tools but the purpose is unambiguous and distinct enough.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus siblings like describe_dataset, list_datasets, or query_series. The description mentions default behavior and the optional top_n, but doesn't state conditions for selecting this tool over others or when not to use it. This is a significant gap.

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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