Use this when the user asks which perpetual markets are tradable on MCPify (Hyperliquid), or for a market overview with prices, 24h change, volume, funding and max leverage.
| Name | Required | Description | Default |
|---|---|---|---|
| sort | No | Sort order | volume |
| limit | No | Max rows | |
| search | No | Filter by symbol substring, e.g. BTC |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only/idempotent safety. The description adds useful behavioral context by scoping the data to MCPify (Hyperliquid) perpetuals and disclosing the returned data fields. No side effects or hidden behavior are omitted that matter for this read-only 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 sentence front-loads the use case and packs the returned-field summary with no filler. Every phrase adds information.
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 list tool with no required parameters, the description plus schema and annotations is fully sufficient: use case, data scope, output fields, and parameter constraints are all covered.
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?
All three parameters have descriptions in the schema (100% coverage). The description adds no extra semantics about sort, limit, or search, so it does not raise the baseline score, but it also does not need to.
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 and resource: listing tradable perpetual markets and providing a market overview. It explicitly lists the fields (prices, 24h change, volume, funding, max leverage), making it easy to distinguish from price, candle, and orderbook siblings.
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 gives explicit triggers ('when the user asks which perpetual markets are tradable' or 'for a market overview'). However, it does not mention when-not-to-use or point to alternatives like get_price or get_candles, so it falls short of a full routing contract.
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.