Hyperliquid

hl_account

Read your own Hyperliquid account: perp positions and margin, spot balances, open orders, fills, fees, or portfolio history. Uses only your bound wallet, so it cannot target another account.

Instructions

Read this agent's own Hyperliquid account: perp positions and margin, spot balances, open orders, recent fills, or portfolio history. The address is always this agent's own bound wallet — it cannot be pointed at another account.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "kind": "<kind>"
    },
    "name": "hl_account"
  }
}

Input Schema

Name	Required	Description	Default
`kind`	Yes	Which account read to perform.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 29, 2026

TDQS

A4.1/5.0

Behavior4/5

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

With no annotations, the description carries the full behavioral burden. It strongly signals a read-only, non-mutating operation via 'Read' and discloses a meaningful ownership/authorization boundary (address is always the bound wallet, never a supplied one). It omits rate limits, data freshness, and any note on large result sets from fills/portfolio history.

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, front-loaded with the resource and the returned data types, followed by the scope constraint. No filler and nothing buried.

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 single-enum, read-only tool with no output schema and no annotations, the description covers what to query and the ownership scope. It stops short of describing return shape or pagination behavior for potentially large reads like userFills and portfolio, which the missing output schema leaves unaddressed.

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, but the description goes further by translating the enum values into user intent: perp positions/margin, spot balances, open orders, recent fills, portfolio history. Only 'userFees' lacks a plain-language mapping, so the mapping is strong but not exhaustive.

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?

States a specific verb (Read) and resource (this agent's own Hyperliquid account) and enumerates the concrete data types retrievable: perp positions/margin, spot balances, open orders, fills, portfolio history. It implicitly separates itself from hl_market_data (account vs market), but offers no differentiation from the sibling hl_account_status, which is the closest competing tool.

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?

Clear context for when to reach for it (fetching the agent's own account reads) and an explicit when-not constraint: 'it cannot be pointed at another account.' No guidance is given on when to prefer hl_account_status over this tool, so the alternative-selection surface is incomplete.

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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