Use this when the user wants OHLCV candle history for a Hyperliquid perp market (for analysis or charting).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of candles | |
| symbol | Yes | Coin symbol, e.g. BTC | |
| interval | No | Candle interval | 1h |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds the 'perp market' scoping and the analysis/charting use case, but it does not disclose additional behavioral details such as ordering, pagination, or whether the current incomplete candle is included.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no filler. The trigger condition 'Use this when the user wants...' is front-loaded, and every phrase contributes to purpose or usage context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only tool with three well-documented parameters and safety annotations, the description is largely complete. The main gap is the lack of any explicit return-format hint or statement about which time window is returned, though 'OHLCV candle history' communicates the core output adequately.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so symbol, limit, and interval are already documented in the schema. The description does not add parameter-level meaning beyond the schema, and the OHLCV framing is too broad to count as extra parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific verb/resource combination: retrieving OHLCV candle history for a Hyperliquid perp market. It clearly distinguishes this from sibling tools like get_price or get_orderbook by focusing on historical candle data for analysis or charting.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use the tool: when the user wants OHLCV candle history for a Hyperliquid perp market. It does not mention explicit exclusions or alternatives, but the usage context is clear enough to route correctly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool maps to a distinct resource/action: market data, account state, resting orders, audit records, fills, order placement, and risk management. The only close pair, preview_order and place_order, is clearly delineated as validation vs execution.
All tool names follow a consistent snake_case verb_noun pattern (get_, place_, cancel_, close_, set_). The object names are uniform and predictable, making the set easy to navigate.
13 tools is well-scoped for a trading server: market data, account/order queries, execution, and position risk each have coverage without redundancy or bloat.
The surface covers the core trading lifecycle: market data, account, preview/place/cancel, close position, TP/SL, and historical records. Minor gaps exist (e.g., no modify-order operation and no standalone position detail endpoint), but agents can work around them via cancel/replace and get_my_account.