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Tessera Analytics - Hyperliquid Market Intelligence

funding_divergence

Rank markets by how far their funding has diverged from a benchmark (default ETH) over a trailing window. metric=rate compares mean daily funding; metric=cum_funding_spread compares funding accumulated over the window. Returns the most diverged in each direction. Markets with no funding reading are excluded from both lists. Descriptive only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
coinsNoRestrict to these markets.
scopeNo`crypto` (the default), `hip3`, or `all`.
top_nNoMarkets per ranked list, 1..50. Defaults to 10.
metricNo`rate` (the default) ranks each market's mean daily funding minus the benchmark's; `cum_funding_spread` ranks funding accumulated over the window, high minus low, minus the benchmark's.
benchmarkNoThe market whose funding is the yardstick. Defaults to ETH.
window_daysNoLength of the trailing window in days, 1..365. Defaults to 7.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
as_ofYes
notesYes
scopeYes
metricYes
sourceYes
benchmarkYes
truncatedYes
coin_countYesHow many markets had a funding reading in the window; markets without one are excluded from both lists and from this count.
window_daysYes
top_negativeYesThe largest divergences toward the benchmark's opposite, most extreme first.
top_positiveYesThe largest divergences away from the benchmark, most extreme first.
benchmark_valueYesThe benchmark's own value for the metric.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description takes on the behavioral disclosure burden and does so well. It states it is "Descriptive only," explains that markets with no funding reading are excluded from both lists, and notes that it returns the most diverged in each direction, which goes beyond the schema.

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?

Four short sentences, each earn its place: the core ranking purpose, the two metric definitions, the output behavior, and the exclusion rule. The most important information is front-loaded and no words are wasted.

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?

For a moderately complex tool with 6 optional parameters and an output schema, the description is nearly complete. It covers purpose, metric alternatives, benchmark default, return shape, and the no-funding exclusion. It only lacks explicit guidance on when to choose a sibling tool, which is minor given the clarity of the rest.

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 some interpretive context around the metric and benchmark concepts, but it largely restates what the schema already documents rather than introducing new parameter semantics.

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 opens with a specific verb and resource: "Rank markets by how far their funding has diverged from a benchmark (default ETH) over a trailing window." It also clarifies the two metric modes and that it returns diverged markets in both directions, making the tool's purpose unmistakable even without relying on the name.

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

Usage Guidelines3/5

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

The behavior is clear enough that an agent can infer when to use this tool, and "Descriptive only" signals it is a read-only analysis. However, it never explicitly contrasts this tool with siblings like query_series, screen_universe, or live_market, nor states when an alternative would be preferable.

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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