Binance MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes, with clear separation between historical data (get_klines), current data (get_market_data, get_futures_funding_rate, get_futures_open_interest), real-time updates (subscribe_market_data), and testing (test_futures_endpoints). However, get_market_data might overlap with the more specific futures tools in scope, causing minor ambiguity.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern using snake_case, such as get_futures_funding_rate, subscribe_market_data, and test_futures_endpoints. This predictability makes it easy for agents to understand and use the tools without confusion.
Tool Count4/5
With 6 tools, the count is reasonable for a Binance market data server, covering key areas like historical and real-time data, futures metrics, and testing. It's slightly lean but functional, as it could benefit from more tools for broader market coverage (e.g., spot trading or account management).
Completeness3/5
The tools provide good coverage for market data retrieval and futures-specific metrics, but there are notable gaps. For a Binance server, missing operations like placing trades, managing orders, or accessing account information limit its completeness, as agents cannot perform full trading workflows.
Average 2.7/5 across 6 of 6 tools scored. Lowest: 1.7/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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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

Behavior1/5
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 but fails to do so. It doesn't indicate whether this is a read/write operation, what it returns, any side effects (e.g., rate limits, authentication needs), or how it interacts with the system. The vague term 'test' offers no actionable behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, vague phrase that is under-specified rather than concise. It lacks front-loaded clarity and fails to earn its place by providing meaningful information. While brief, it doesn't achieve conciseness through efficiency but through omission of essential details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (implied by 'test' and futures endpoints), lack of annotations, and no output schema, the description is completely inadequate. It doesn't explain what 'test' entails, what results to expect, or how it fits with sibling tools, leaving significant gaps for an AI agent to understand and use the tool 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?
The description adds no parameter semantics beyond what the input schema provides. Since schema description coverage is 100% (the 'symbol' parameter is documented as 'Trading pair symbol (e.g., BTCUSDT)'), the baseline score is 3. The description doesn't clarify usage, constraints, or examples related to the parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Test individual futures endpoints' is a tautology that essentially restates the tool name 'test_futures_endpoints' without specifying what action is performed. It doesn't identify a specific verb (e.g., validate, ping, verify) or resource (e.g., API endpoints, connectivity), making the purpose vague and indistinguishable from sibling tools like 'get_futures_funding_rate' or 'get_klines'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
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 any context, prerequisites, or exclusions, nor does it reference sibling tools like 'get_market_data' or 'subscribe_market_data' that might serve similar purposes. This leaves the agent with no information to make an informed selection.
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 full burden for behavioral disclosure. While 'Get' implies a read operation, it doesn't specify whether this is real-time or delayed data, rate limits, authentication requirements, error conditions, or what format the funding rate is returned in. The description is minimal and lacks important operational context.
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 communicates the core purpose without any wasted words. It's appropriately sized for a simple tool with one parameter and gets straight to the point with no unnecessary elaboration.
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 financial data tool with no annotations and no output schema, the description is insufficient. It doesn't explain what a funding rate is, how it's calculated, what units it's returned in, or what the response structure looks like. The agent would need to guess about important operational details despite the tool's apparent 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?
The schema has 100% description coverage, with the 'symbol' parameter well-documented in the schema itself. The description adds no additional parameter information beyond what's already in the schema, so it meets the baseline for high schema coverage but doesn't provide extra value like format examples or validation rules.
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 current funding rate') and resource ('for a futures trading pair'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'get_futures_open_interest' or 'get_market_data', but the specific focus on funding rates provides implicit distinction.
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_futures_open_interest' or 'get_market_data'. It doesn't mention prerequisites, timing considerations, or any context about when funding rate data is needed versus other market metrics.
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 doesn't describe any behavioral traits such as rate limits, authentication requirements, response format, or potential errors. This is inadequate 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 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.
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 doesn't address what the tool returns (e.g., numerical value, timestamp, additional metadata) or any behavioral aspects like error handling. For a tool with no structured data beyond the input schema, this leaves significant gaps for an agent.
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 parameter 'symbol' fully documented in the input schema. The description doesn't add any meaningful semantics beyond what the schema already provides (e.g., it doesn't clarify format constraints or provide examples beyond the schema's 'e.g., BTCUSDT'), so it 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 current open interest') and the resource ('for a futures trading pair'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'get_futures_funding_rate' or 'get_market_data', 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 provides no guidance on when to use this tool versus alternatives like 'get_futures_funding_rate' or 'get_market_data'. There's no mention of prerequisites, context, or exclusions, leaving the agent to infer usage from the tool name alone.
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 'Get historical candlestick data', implying a read-only operation, but doesn't cover aspects like rate limits, authentication needs, error handling, or data freshness, which are critical for a financial data 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, efficient sentence with no wasted words. It's front-loaded with the core purpose, making it easy to scan and understand quickly, which is ideal for conciseness.
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 financial data retrieval and the lack of annotations and output schema, the description is insufficient. It doesn't explain return values, error conditions, or behavioral traits like pagination or data limits, leaving significant gaps 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?
The schema description coverage is 100%, with all parameters well-documented in the schema itself. The description adds no additional meaning beyond the schema, such as explaining parameter interactions or providing examples, so it meets the baseline for high coverage without compensating 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 'Get' and the resource 'historical candlestick data', which is specific and unambiguous. However, it doesn't distinguish this tool from sibling tools like 'get_market_data' or 'subscribe_market_data', which might also provide market-related information.
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 like 'get_market_data' or 'subscribe_market_data', nor does it specify use cases, prerequisites, or exclusions, leaving the agent to infer usage from context alone.
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 'gets' data, implying a read-only operation, but doesn't cover critical aspects like rate limits, authentication needs, data freshness, or error handling. For a tool with no annotations, this leaves significant gaps in understanding its 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 purpose without any fluff. It's appropriately sized and front-loaded, making it easy to parse quickly, with every word contributing to understanding the core function.
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 market data tools and the lack of annotations and output schema, the description is insufficient. It doesn't explain what 'comprehensive market data' includes (e.g., prices, volumes), return formats, or how it differs from siblings. For a tool with no structured behavioral data, more context is needed to guide 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?
The description adds no specific meaning beyond what the input schema provides. With 100% schema description coverage, the schema already documents both parameters ('symbol' and 'type') clearly, including examples and enums. The description doesn't elaborate on parameter interactions or usage, so it meets the baseline for high schema coverage without adding 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 tool's purpose with a specific verb ('Get') and resource ('comprehensive market data for a trading pair'), making it easy to understand what it does. However, it doesn't explicitly differentiate from sibling tools like 'get_klines' or 'get_futures_funding_rate', which also retrieve market-related 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. With siblings like 'get_klines' (likely for historical data) and 'get_futures_funding_rate' (specific to futures), there's no indication of context, prerequisites, or exclusions, leaving the agent to guess based on tool names alone.
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 updates' and 'subscribe,' hinting at a streaming or push-based operation, but fails to detail critical aspects like authentication requirements, rate limits, how to handle or stop subscriptions, or the format of incoming data. 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, efficient sentence that front-loads the core purpose ('subscribe to real-time market data updates') with zero wasted words. It is appropriately sized for the tool's complexity, making it easy to parse and understand quickly without unnecessary elaboration.
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 (involving real-time subscriptions with multiple parameters) and the absence of both annotations and an output schema, the description is incomplete. It lacks details on behavioral traits (e.g., how updates are delivered, error handling), does not explain return values or subscription management, and fails to provide context on when to use it versus siblings. This leaves the agent with insufficient information for effective 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 all three parameters with enums and examples. The description adds no additional parameter semantics beyond what the schema provides, such as explaining interactions between parameters or usage nuances. However, with high schema coverage, a baseline score of 3 is appropriate as the schema adequately handles parameter documentation.
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 ('subscribe to') and resource ('real-time market data updates'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'get_market_data' (which likely retrieves static data), but the 'subscribe' verb implies a continuous stream versus a one-time fetch, providing some implicit distinction.
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?
No explicit guidance is provided on when to use this tool versus alternatives. The description lacks context about prerequisites (e.g., authentication), frequency of updates, or comparisons to sibling tools like 'get_market_data' for static data. Usage is implied by the verb 'subscribe,' but no when-not-to-use scenarios or clear alternatives are mentioned.
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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