OKX MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
The tools are mostly distinct with clear purposes: get_candlesticks for historical data, get_live_ticker for real-time data, get_price for current price, and subscribe/unsubscribe_ticker for WebSocket management. However, get_live_ticker and get_price could be confused as both provide current price information, though one is WebSocket-based and the other is a simple fetch.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with snake_case, such as get_candlesticks, get_live_ticker, and subscribe_ticker. This predictability makes it easy for an agent to understand and use the tools without confusion.
Tool Count5/5
With 5 tools, the server is well-scoped for its purpose of providing OKX market data and real-time updates. Each tool serves a specific function, and there are no redundant or unnecessary additions, making the count appropriate for the domain.
Completeness4/5
The toolset covers core market data operations well, including historical data, real-time updates, and subscription management. A minor gap exists in the lack of tools for order placement or account management, but for a data-focused server, the coverage is sufficient for typical agent workflows.
Average 2.9/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- No high-severity vulnerability alerts
- No code scanning findings
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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 hints at 'visualization options' but doesn't specify what these entail (e.g., formatting effects, rate limits, or data freshness). This leaves gaps in understanding how the tool behaves beyond basic functionality.
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, efficient sentence that front-loads the core purpose. It could be slightly more structured by separating visualization details, but it avoids unnecessary verbosity and earns its place.
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 no annotations, no output schema, and a tool with multiple parameters for financial data retrieval, the description is incomplete. It lacks details on return values, error handling, or practical constraints (e.g., data latency), leaving significant gaps for effective agent 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?
The input schema has 100% description coverage, so parameters are well-documented there. The description adds minimal value by implying 'visualization options' relate to the 'format' parameter, but doesn't elaborate on semantics beyond what the schema provides. This meets the baseline for high schema coverage.
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 an OKX instrument'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_live_ticker' or 'get_price' in terms of data type or granularity, which prevents 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 mentions 'visualization options' but provides no guidance on when to use this tool versus alternatives like 'get_live_ticker' or 'get_price'. There are no explicit when-to-use or when-not-to-use instructions, leaving usage context 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 mentions 'WebSocket subscription' which implies real-time or streaming data, but doesn't specify latency, rate limits, authentication needs, or what happens if the subscription fails. For a tool with no annotations, this leaves significant gaps in understanding its operational behavior.
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 function without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse quickly.
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 complexity of real-time data tools and the absence of both annotations and an output schema, the description is insufficient. It doesn't explain the return format, error conditions, or how the WebSocket integration works, leaving the agent with incomplete context for reliable tool invocation.
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?
The input schema has 100% description coverage, clearly documenting both parameters ('instrument' and 'format') with details like default values. The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline score of 3 where the schema does the heavy lifting.
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 ('latest ticker data from WebSocket subscription'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_price' or 'get_candlesticks', which likely provide similar financial data but through different mechanisms or timeframes.
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_price' or 'subscribe_ticker'. It mentions 'WebSocket subscription' but doesn't clarify if this is for real-time data only or how it differs from other data retrieval methods, leaving the agent to infer usage context.
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 must fully disclose behavioral traits. It mentions 'formatted visualization,' which hints at output behavior, but lacks details on critical aspects like whether this is a read-only operation, potential rate limits, authentication needs, or error handling. The description is too vague to adequately inform the agent about how the tool behaves beyond its basic function.
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 is front-loaded with the core action ('Get latest price') and includes the key feature ('formatted visualization'). There is no wasted information, 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 for a tool that retrieves financial data. It does not address important contextual elements such as the source of the data (e.g., real-time vs. cached), potential limitations, error scenarios, or the structure of the formatted output. This leaves gaps in understanding the tool's full behavior and constraints.
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?
The input schema has 100% description coverage, with clear documentation for both parameters ('instrument' and 'format'). The description adds minimal value beyond the schema, as it does not explain parameter semantics further (e.g., what 'formatted visualization' entails for the 'format' parameter). With high schema coverage, the baseline score of 3 is appropriate, as the description does not compensate with additional insights.
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 tool's purpose: 'Get latest price for an OKX instrument with formatted visualization.' It specifies the verb ('Get'), resource ('latest price for an OKX instrument'), and includes the unique aspect of 'formatted visualization.' However, it does not explicitly differentiate from siblings like 'get_candlesticks' or 'get_live_ticker,' which might also retrieve price-related data, preventing a score of 5.
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 does not mention sibling tools like 'get_candlesticks' (for historical data) or 'get_live_ticker' (for real-time updates), nor does it specify contexts or exclusions. This lack of comparative usage information limits the agent's ability to select the correct tool.
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 mentions 'real-time ticker updates,' implying a streaming or ongoing operation, but fails to detail critical aspects like whether this initiates a persistent connection, requires authentication, has rate limits, or what happens on subscription (e.g., callback mechanisms or data format). This leaves significant gaps for an agent to understand the tool's behavior.
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, clear sentence with zero wasted words, making it highly concise and front-loaded. Every part of the sentence directly contributes to understanding the tool's purpose, earning its place effectively.
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 complexity of a subscription tool (implying ongoing data flow) with no annotations and no output schema, the description is incomplete. It doesn't explain what 'subscribe' entails operationally (e.g., how updates are delivered, response format, or error handling), leaving the agent with insufficient information to use the tool correctly in context.
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?
The input schema has 100% description coverage, with the 'instrument' parameter fully documented in the schema. The description adds no additional semantic context beyond what the schema provides (e.g., it doesn't clarify the scope of 'real-time' or provide examples beyond the schema's 'BTC-USDT'). Thus, it meets the baseline for high schema coverage without adding extra value.
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 verb ('Subscribe to') and resource ('real-time ticker updates for an instrument'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_live_ticker' or 'unsubscribe_ticker', which would be needed for 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 like 'get_live_ticker' (for one-time retrieval) or 'unsubscribe_ticker' (for stopping updates). It also lacks context about prerequisites, such as whether a subscription is needed before use or how it relates to other tools.
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 the action but doesn't describe what 'unsubscribe' entails—whether it stops updates immediately, affects other users, requires specific permissions, or has side effects. This leaves critical behavioral traits undocumented for a mutation tool.
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, clear sentence with no wasted words. It's front-loaded with the core action and resource, making it easy to parse quickly. Every part of the sentence contributes directly to understanding the tool's purpose.
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?
For a mutation tool with no annotations and no output schema, the description is incomplete. It lacks information about what happens after unsubscribing (e.g., confirmation, error handling), behavioral details, and how it integrates with sibling tools. This leaves significant gaps for an agent to use it correctly in context.
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?
The schema description coverage is 100%, with the single parameter 'instrument' well-documented in the schema. The description adds no additional semantic context about the parameter, such as format examples beyond what's in the schema or constraints like valid instrument types. This meets the baseline for high schema coverage.
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 ('Unsubscribe from') and the resource ('real-time ticker updates for an instrument'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from its sibling 'subscribe_ticker' beyond the obvious opposite action, missing an opportunity to clarify scope or relationship.
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 or prerequisites. It doesn't mention if it should only be called after subscribing via 'subscribe_ticker', what happens if unsubscribing from a non-subscribed instrument, or any context about its role relative to sibling tools like 'get_live_ticker'.
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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