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 useful context about the live venue returning truthful price, size, fee, and realized PnL, which is valuable given there is no output schema.
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 immediately states when to use the tool and what it returns. Every word earns its place, with no filler or redundant restatement of the tool name.
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 tool with one optional parameter and annotations covering safety, the description is nearly complete. It names the key returned fields and the live-venue context, though it could make explicit that the result is an array of fills.
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 coverage is 100% and the only parameter, limit, is fully described as 'Number of fills' with default, min, and max. The description adds no additional parameter-level meaning, so baseline 3 is appropriate.
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 states a specific verb ('get'), resource ('trade history'), and distinguishes the operation as fills/executions rather than orders. The mention of 'Hyperliquid' and 'live venue truth' differentiates it from related order-record tools without ambiguity.
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 clear context. It does not explicitly name alternatives or exclusions, but the fills/executions wording sets it apart from siblings like get_my_order_records.
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.