hyperliquid-agent-gateway

by alekskram

Overview Schema Source Related Servers Discussions Admin

Python

Remote

Candles

candles

Read-only

Retrieve OHLCV candlestick data for any coin with configurable interval and limit, returning newest-first rows.

Instructions

OHLCV candles from candleSnapshot for coin: rows newest-first, capped at limit (max 500). startTime is computed so roughly limit candles of interval are requested. Valid intervals: 1m, 15m, 1h, 4h, 1d, 1w, 1M. Example: candles(coin="BTC", interval="4h", limit=50)

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": {
      "coin": "<coin>"
    },
    "name": "candles"
  }
}

Input Schema

Name	Required	Default
`coin`	Yes
`limit`	No
`interval`	No	1h

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.1Sep 6, 2026

TDQS

A4.5/5.0

Behavior5/5

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

Beyond the annotations, the description discloses meaningful behavioral details: rows are newest-first, results are capped at limit, startTime is auto-computed to request roughly limit candles, and max limit is 500. This gives the agent valuable insight into how the tool behaves before calling it.

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 is compact and front-loaded: it opens with the core purpose, then packs ordering, limiting, interval options, and an example into two sentences. Every sentence adds useful information with no filler.

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 output schema exists and annotations cover safety profile, the description provides enough contextual detail: parameter behavior, valid intervals, ordering, and a concrete example. Nothing essential for selecting or invoking this tool is missing.

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?

With 0% schema description coverage, the description must explain parameters, and it largely does: coin is demonstrated via example, limit is explained as a cap with a max of 500, and interval gets an explicit list of valid values. Coin's exact format is only shown through the example rather than stated directly.

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 states a specific verb and resource: returns OHLCV candles from candleSnapshot for a given coin. It also specifies ordering and limits, making it clearly distinct from sibling tools like trades, quote, and order_book, which serve different market data purposes.

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 and output are clearly described, so an agent can infer this tool is for historical OHLCV data. However, it does not explicitly say when to prefer this over sibling alternatives or mention any exclusions, leaving usage guidance mostly implied.

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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