dydx-agent-gateway

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Candles

candles

Read-only

Retrieve historical OHLCV candles with open interest for any market. Choose from 1-minute to 1-day resolutions to analyze price action, volume, and positioning.

Instructions

OHLCV candles with open interest for a market. resolution: 1MIN|5MIN|15MIN|30MIN|1HOUR|4HOURS|1DAY. Each candle: startedAt, open/high/low/close (price), baseTokenVolume (base-coin size), usdVolume (USD), startingOpenInterest. Rows are ordered oldest -> newest (the indexer sends newest-first; we normalize). limit is capped at 1000. Example: candles(ticker="ETH-USD", resolution="1HOUR", limit=100)

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

Input Schema

Name	Required	Default
`limit`	No
`ticker`	Yes
`resolution`	No	1HOUR

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.3.0Sep 5, 2026

TDQS

A3.9/5.0

Behavior4/5

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

Annotations already mark this as read-only and non-destructive. The description adds useful behavioral context beyond that: rows are normalized to oldest->newest even though the indexer sends newest-first, and limit is capped at 1000. This is meaningful and not contradicted by annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but every sentence contributes: candle fields, ordering behavior, limit cap, and an example. The resolution list slightly duplicates the schema enum, but its placement is efficient and front-loaded.

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?

For a simple 3-parameter read-only tool with no output schema, the description is complete: it explains what each candle contains, the ordering guarantee, the limit cap, and the resolution options. An agent has enough to call it correctly and interpret the result.

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 description coverage is 0%, so the description carries the burden for parameter meaning. It covers resolution with the full enum list, limit with its 1000 cap, and ticker with a concrete 'ETH-USD' example. It could be more explicit about ticker being the market identifier, but it compensates well overall.

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?

Description clearly states the tool returns OHLCV candles with open interest for a market, and enumerates the candle fields. It lacks an explicit verb like 'fetch' or 'return' and does not distinguish itself from sibling tools, so it falls short of a 5.

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 description shows invocation with an example and lists valid resolutions, which implies usage context for market candle data. However, it never explicitly states when to prefer this over sibling tools like market_ta, cvd, or recent_trades, nor does it give exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.