Binance.US MCP Server

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 that the tool 'Returns OHLCV data,' which indicates a read-only operation, but does not specify other important traits such as rate limits, authentication needs, error handling, or whether it's a public or private API endpoint. For a financial data 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise and front-loaded, consisting of only two sentences that directly state the tool's purpose and output. Every word earns its place with no redundancy or unnecessary details, making it easy for an AI 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 (5 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and output but lacks context on usage guidelines, behavioral traits, and integration with sibling tools. Without an output schema, it does not detail the structure of returned OHLCV data, which could be important for downstream processing. This results in a description that meets the minimum viable standard but has clear gaps.

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 schema description coverage is 100%, meaning all parameters are well-documented in the input schema with descriptions and constraints (e.g., 'symbol' as trading pair, 'interval' with enum values, 'limit' with default and max). The description adds no additional parameter semantics beyond what the schema provides, such as examples of valid symbol formats or time range best practices. Given the high schema coverage, a baseline score of 3 is appropriate.

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 tool's purpose: 'Get Kline/candlestick data for a trading pair. Returns OHLCV data.' It specifies the verb ('Get'), resource ('Kline/candlestick data'), and output type ('OHLCV data'), which is specific and informative. However, it does not explicitly differentiate this tool from sibling tools like 'binance_us_historical_trades' or 'binance_us_recent_trades', which might also retrieve trading data, so it lacks sibling differentiation.

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 does not mention any prerequisites, such as authentication requirements, or compare it to other data retrieval tools in the sibling list (e.g., 'binance_us_historical_trades' for historical trades or 'binance_us_ticker_24hr' for 24-hour ticker data). Without such context, an AI agent might struggle to choose the appropriate tool for specific scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.