Hyperliquid MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| get_all_midsA | Get mid prices for all coins on Hyperliquid |
| get_candle_snapshotB | Get candlestick data for a token on Hyperliquid |
| get_l2_bookC | Get the L2 book of a token on Hyperliquid |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 3 tools
Each tool has a clearly distinct purpose: get_all_mids retrieves mid prices for all coins, get_candle_snapshot provides candlestick data for a specific token, and get_l2_book fetches the L2 order book for a token. There is no overlap or ambiguity in functionality, making tool selection straightforward for an agent.
All tool names follow a consistent verb_noun pattern with 'get_' as the prefix, followed by a descriptive noun phrase (e.g., get_all_mids, get_candle_snapshot, get_l2_book). This uniformity enhances readability and predictability across the tool set.
With only 3 tools, the server feels thin for a trading or market data domain, which typically involves more operations like placing orders, checking balances, or managing positions. While the tools are well-defined, the count is borderline low for comprehensive coverage of such a scope.
The tool set is severely incomplete for a trading platform server, as it lacks essential operations such as order placement, account information retrieval, trade execution, or portfolio management. This creates significant gaps that will likely cause agent failures in real-world trading scenarios.