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mektigboy

Hyperliquid MCP Server

by mektigboy
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    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.

    Naming Consistency5/5

    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.

    Tool Count3/5

    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.

    Completeness2/5

    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.

  • Average 3/5 across 3 of 3 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • 0 of 1 community issues answered or closed in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior2/5

    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.

    Conciseness5/5

    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.

    Completeness2/5

    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.

    Parameters3/5

    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.

    Purpose4/5

    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.

    Usage Guidelines2/5

    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.

  • Behavior2/5

    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.

    Conciseness5/5

    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.

    Completeness2/5

    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.

    Parameters4/5

    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.

    Purpose4/5

    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.

    Usage Guidelines2/5

    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.

  • Behavior2/5

    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.

    Conciseness5/5

    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.

    Completeness3/5

    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.

    Parameters3/5

    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.

    Purpose4/5

    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.

    Usage Guidelines2/5

    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.

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