hyperevm-mcp

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Server Quality Checklist

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Latest release: v1.4.0
Disambiguation5/5
Each tool targets a distinct aspect of the Hyperliquid/HyperEVM ecosystem: yields, protocols, market prices, staking, wallet info, fees, funding history, and order book. There is no overlap, and descriptions clearly differentiate them.
Naming Consistency5/5
Tools consistently use 'hyperevm_' for EVM-related data and 'hl_' for native Hyperliquid data, followed by a clear noun describing the resource. The pattern is predictable and logical.
Tool Count5/5
With 8 tools, the server covers the core functionalities expected for a read-only data provider on this ecosystem. The count feels well-scoped—neither too many nor too few for the domain.
Completeness5/5
The tool set covers major data needs (yields, protocols, market state, staking, wallet, fees, funding history, order book) for a read-only interface. No obvious gaps for public data queries; every important area is addressed.
Average 4.5/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 13 commits in the last 12 weeks
- Last stable release on July 26, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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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

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, destructiveHint false. Description adds context: 'Read-only, public data, no API key' and explains the semantics of fees vs TVL. No contradictions.
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?
Four sentences, front-loaded with purpose, no unnecessary words. Each sentence adds value: purpose, comparison, usage, and safety note.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given simple tool with rich annotations and full schema coverage, description covers data scope and usage. No output schema, but return values are self-explanatory (fee lines). No mention of pagination or errors, but adequate for the tool's simplicity.
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 covers both parameters fully (100% description coverage). Description adds minimal extra meaning: clarifies that omitting name yields ranked list. No additional syntax or constraints beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool lists fees paid to protocols on Hyperliquid over three time periods, ranked. It distinguishes from sibling tools like hyperevm_yields by contrasting fees vs TVL, and the name parameter aligns with the resource 'protocols'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear usage: pass a protocol name for a single line, omit for ranked list. Mentions read-only, public data, no API key. Does not explicitly state when to prefer this over alternatives, but the contrast with TVL gives implicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint false), the description adds critical context: it reads the public ledger, no key/signature/send path, ensuring agents understand it is purely observational and safe. This fully satisfies the transparency requirement.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single paragraph that front-loads the action and efficiently lists returned data. It is concise without being terse, though structured bullet points could improve readability slightly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description comprehensively covers the tool's purpose, usage constraints, and behavioral traits given its low complexity and rich annotations. It lacks an explicit output schema description but lists returned fields sufficiently for agent understanding.
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 both parameters (address, limit). The description does not add extra meaning for parameters beyond listing what data is returned. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Look up what any public Hyperliquid address holds' and enumerates specific data returned (account value, margin, positions, etc.). It distinguishes itself from sibling tools like hl_market or hyperevm_yields by focusing on wallet holdings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states it works on any address and explains the read-only nature ('the server has no key, no signature and no send path'), implying when to use (lookup) and when not to (actions requiring write). However, it does not directly reference sibling tools as alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnlyHint, idempotentHint), the description adds that the tool is public and requires no API key. It also explains the behavior when size_usd is provided, including walking the book and returning slippage or a depth insufficiency statement.
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 three sentences, each adding value. It front-loads the purpose, explains the key parameter behavior, and states the use case. No redundant words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description conceptually covers the outputs (spread, top of book, side sizes, fill price, slippage). It lacks exact format details, but for a market data tool, this is nearly complete.
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?
Schema coverage is 100%, but the description adds significant meaning: for size_usd, it explains the trade simulation and output; for market, it notes the default is perp. This enhances understanding beyond the schema alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool provides resting order book depth, including spread, top of book, and side sizes. It distinguishes itself from price and volume alone, making its purpose precise and actionable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use it: to assess trade feasibility ('can I actually get in or out at this size'). While it doesn't explicitly list alternatives, the context is clear and sufficient for correct usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, non-destructive. Description adds 'Read-only, public data, no API key,' reinforcing safety and accessibility. No contradictions.
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?
Three sentences, front-loaded with purpose, each sentence adds value. No redundancy, perfectly sized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers input and output expectations (TVL, growth, chain total, detail card). Lacks explicit return format, but given parameter simplicity and annotations, it's sufficiently complete.
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?
Schema covers 100% of parameters. Description adds functional context: name triggers detail card with audit links, sort options map to enum, limit default is 15. Enhances rather than repeats.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists protocols on HyperEVM with TVL, category, growth, and chain total, with sorting and detail card options. It distinguishes from siblings like hyperevm_yields or hl_market.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains when to use (get protocol data) and how to use (omit name for list, specify name for detail, choose sort order). It lacks explicit when-not-to-use or alternative comparisons, but context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, openWorldHint, etc., but the description adds context: includes Binance/Bybit comparison, historical behavior details. No contradiction with annotations. Slightly reduced because annotations already cover safety profile.
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, front-loaded paragraph of four sentences. Every sentence provides essential information without redundancy, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, but description sufficiently explains return data (average, range, positivity, holding cost, Binance/Bybit comparison). For a read-only data retrieval tool with strong annotations, this is complete.
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%, baseline 3. The description does not add extra meaning beyond the schema—it only implicitly mentions 'how far back' which is already described in the schema. No additional semantic value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it provides funding rate history for a Hyperliquid perp, specifying metrics like average, range, positivity, and holding cost. It distinguishes from just current rate by highlighting historical behavior, which differentiates it from siblings like hl_market.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly advises 'Use this when the question is whether a funding spread is durable rather than what it is this hour.' Also states it is read-only, public data, no API key needed, providing clear when-to-use and preconditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false. The description adds context: 'Read-only, public data, no API key,' which reinforces safety and accessibility. It also describes the output fields (prices, funding, OI, etc.) beyond what annotations provide.
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?
Three sentences, each purposeful: first lists what the tool returns, second explains how to use with a symbol, third gives temporal context and links to alternative. Front-loaded with essential information, no unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description adequately covers return data, usage patterns, and alternative tool. It could mention pagination or sorting but those are minor. With strong annotations, this is sufficiently complete for a read-only data query tool.
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?
Schema coverage is 100% with descriptions. The description adds value by providing examples (e.g., 'BTC or PURR') and clarifying default behaviors ('Rows in the list. Default 10,' 'Default perp,' 'Omitted = top by volume'). This goes beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it provides current prices and market state on Hyperliquid for perp and spot, listing specific data points. It distinguishes itself from the sibling hl_funding tool by noting it is a current snapshot versus past funding behavior.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly tells when to use: for current snapshot, and when not: for past funding data, directing to hl_funding. Also explains behavior when symbol is omitted (top by volume) and when symbol is provided (shows predicted funding side by side with Binance and Bybit).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, openWorld, idempotent, non-destructive. Description reinforces with 'Read-only, public data, no API key' and explains data structure and separation of yield types, adding value beyond annotations.
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?
Four sentences, front-loaded with core purpose, no wasted words. Efficiently conveys purpose, categories, usage guidance, and data characteristics.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, description covers expected output (ranked table with categories, yield separation). Mentions alternative tool. Lacks exact column names but sufficient for an agent to understand what the tool returns.
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 coverage is 100% with clear parameter descriptions. The tool description sets overall context but does not add individual parameter details beyond the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists all yield opportunities on Hyperliquid/HyperEVM, ranked, covering specific categories (LSTs, lending, LP/looping). It distinguishes from siblings like hyperevm_pool_history (mentioned) and by context from hyperevm_protocols, hl_market, hl_staking.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Start here for where is the best yield' and suggests hyperevm_pool_history for historical verification. Also notes it's read-only, public, no API key, giving clear usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds context: it's public data, no API key, and details what data is returned (active set, jailed excluded, etc.), which goes beyond annotations. No contradiction.
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 two sentences with no wasted words. It front-loads the core purpose and efficiently provides context about why to use this tool vs liquid staking. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description explains what the tool returns (active set sorted by APR, commission, uptime, share of stake, jailed excluded). It contrasts with liquid staking yields. With good annotations and sibling differentiation, the description is fully adequate for a read-only list tool.
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 coverage is 100% for the two parameters, each with descriptions in the schema. The tool description does not add additional meaning beyond the schema, but the baseline is 3 due to high coverage. The description focuses on output context rather than parameter details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it is about staking HYPE directly with a validator, listing active set ranked by predicted APR with commission, uptime, share of stake, and naming jailed validators. It distinguishes from liquid staking wrappers via mention of hyperevm_yields, making the purpose specific and differentiated from siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says this is the benchmark for liquid staking rates and those live in hyperevm_yields, providing clear guidance on when to use this tool vs alternatives. It also states it is read-only public data with no API key.
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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