Hyperliquid MCP Server
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Hyperliquid MCP Serverget candle snapshot for ETH with 1h interval from yesterday"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Hyperliquid MCP Server
An MCP server implementation that integrates the Hyperliquid SDK.
Tools
get_all_mids
Retrieve mid prices for all coins on Hyperliquid
No required inputs
get_candle_snapshot
Get historical candlestick data for any token
Inputs:
coin(string): Token symbolinterval(string): Time interval (e.g., '15m', '1h')startTime(number): Start time in milliseconds since epochendTime(number, optional): End time in milliseconds since epoch
get_l2_book
Access the L2 order book for any token
Inputs:
symbol(string): Token symbol
Usage with Claude Desktop
Add this to your claude_desktop_config.json:
NPX
{
"mcpServers": {
"brave-search": {
"command": "npx",
"args": ["-y", "@mektigboy/server-hyperliquid"]
}
}
}Related MCP server: 0xarchive-mcp
License
This MCP server is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.
Available Tools
3 toolsget_all_midsB
Get mid prices for all coins on Hyperliquid
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool does but lacks details on traits such as whether it's read-only, requires authentication, has rate limits, or what the output format might be. This is a significant gap for a tool with zero annotation coverage.
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, efficient sentence that directly states the tool's purpose without any wasted words. It's front-loaded and appropriately sized for a simple tool, making it highly concise and well-structured.
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?
Given the lack of annotations and output schema, the description is incomplete. It explains what the tool does but fails to provide necessary context such as behavioral traits, output format, or usage guidelines relative to siblings. For a tool with no structured data support, this leaves significant gaps in understanding.
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?
The tool has 0 parameters, and schema description coverage is 100%, so there are no parameters to document. The description doesn't need to add parameter semantics, and it appropriately doesn't mention any. A baseline of 4 is applied since no parameters exist, and the description doesn't introduce confusion.
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 states the action ('Get') and resource ('mid prices for all coins on Hyperliquid'), making the tool's purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_candle_snapshot' or 'get_l2_book', which likely serve different purposes (e.g., retrieving candle data or order book data), so it falls short of a perfect score.
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 provides no guidance on when to use this tool versus alternatives. It doesn't mention scenarios where this tool is preferred over siblings like 'get_candle_snapshot' or 'get_l2_book', nor does it specify prerequisites or exclusions. This lack of context leaves usage unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_candle_snapshotB
Get candlestick data for a token on Hyperliquid
| Name | Required | Description | Default |
|---|---|---|---|
| coin | Yes | The symbol of the token to get candlestick data for | |
| interval | Yes | Time interval (e.g., '15m', '1h') | |
| startTime | Yes | Start time in milliseconds since epoch | |
| endTime | No | End time in milliseconds since epoch (optional) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool's purpose but doesn't describe behavioral traits such as whether it's read-only, potential rate limits, authentication needs, or what happens with missing data. This leaves significant gaps for a tool with no annotation coverage.
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, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy for an agent to parse quickly.
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?
Given the tool's moderate complexity (4 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on usage, behavioral context, or output format, which would be helpful for an agent to invoke it correctly.
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 the schema already documents all parameters thoroughly. The description doesn't add any additional meaning beyond what the schema provides, such as examples of valid intervals or token symbols, which keeps it at the baseline score.
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 states the action ('Get candlestick data') and resource ('for a token on Hyperliquid'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'get_all_mids' or 'get_l2_book', which likely serve different purposes in the same domain.
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 provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools or contexts where this tool is appropriate, leaving the agent to infer usage based on the name and parameters alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_l2_bookC
Get the L2 book of a token on Hyperliquid
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | No | The symbol of the token to get the price of | |
| required | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves data ('Get'), implying a read-only operation, but doesn't specify whether it's real-time or historical, rate limits, authentication needs, or what an L2 book entails (e.g., bid/ask levels). This leaves significant gaps for a tool with no annotation coverage.
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, efficient sentence with zero wasted words. It front-loads the core purpose and includes essential context ('on Hyperliquid'), making it highly concise and well-structured for quick understanding.
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?
Given the tool's complexity (financial data retrieval), lack of annotations, no output schema, and incomplete parameter documentation, the description is insufficient. It doesn't explain what an L2 book contains, the response format, or behavioral traits like latency or data freshness, leaving the agent with critical gaps for effective use.
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 50% (one parameter documented, one not). The description adds no parameter details beyond the schema, which already documents 'symbol' as 'The symbol of the token to get the price of'. Since the schema handles half the parameters adequately, the baseline score of 3 applies, as the description doesn't compensate for the undocumented parameter.
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 states the action ('Get') and the resource ('L2 book of a token'), specifying the domain ('on Hyperliquid'). It distinguishes from siblings by focusing on order book data rather than mid prices or candle snapshots. However, it doesn't explicitly differentiate from potential similar tools beyond the given 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?
The description provides no guidance on when to use this tool versus alternatives like get_all_mids or get_candle_snapshot. It lacks context about use cases, prerequisites, or exclusions, leaving the agent to infer usage based solely on tool names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
3 tool updates
- First observed
get_all_mids - First observed
get_candle_snapshot - First observed
get_l2_book
TDQS
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.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
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