get_my_trade_history
Use this when the user asks for their recent fills/executions on Hyperliquid (live venue truth: price, size, fee, realized PnL).
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of fills |
Use this when the user asks for their recent fills/executions on Hyperliquid (live venue truth: price, size, fee, realized PnL).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of fills |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds context about 'live venue truth' and the returned fields, but does not disclose pagination, data retention, or response shape beyond the field list.
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?
A single, front-loaded sentence that states the trigger and the key returned data with zero filler. Every word earns its place.
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 one optional parameter and annotations covering side effects, the description is sufficient. It lists the important return fields despite no output schema, and the trigger phrase covers user intent.
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 covers the only parameter (limit) fully with description, default, min, and max. The description adds no further parameter-level detail, so the baseline 3 applies.
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 clearly identifies the resource ('recent fills/executions') and the specific venue (Hyperliquid), with a concrete list of returned fields. It distinguishes this tool from siblings like get_my_order_records by emphasizing filled trades rather than orders.
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?
It explicitly says 'Use this when the user asks for their recent fills/executions', giving a clear trigger condition. It does not mention alternatives or when not to use it, but the condition is specific 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 targets a distinct resource or action, but get_markets and get_price overlap in the data they return (price, 24h change, volume, funding), differing mainly by all-markets vs single-coin scope. The descriptions are explicit enough that an agent should rarely misselect, though the boundary is slightly blurry.
All tool names follow a consistent verb_noun snake_case pattern: get_ for reads, plus clear action verbs like place_, preview_, cancel_, close_, and set_. There is no mixing of styles or vague generic verbs.
13 tools is well within the ideal range for a trading-focused server. Each tool covers a distinct part of the workflow without feeling bloated or redundant.
The tool surface covers the full trading lifecycle: market data, account/position/order/trade reads, order preview and placement, cancellation, position closing, and TP/SL management. No obvious dead-ends or missing core operations for the stated Hyperliquid perp trading purpose.