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Muvon

mcp-binance-futures

by Muvon

get_klines

Retrieve OHLCV candlestick data for Binance USDT-M Futures trading pairs to analyze price movements, volume, and market trends.

Instructions

Get OHLCV candlestick data for a symbol.

Returns list of dicts with: openTime, open, high, low, close, volume, closeTime, quoteVolume, trades, takerBuyVolume, takerBuyQuoteVolume.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYesTrading pair, e.g. 'BTCUSDT'
intervalNoCandlestick interval1h
limitNoNumber of candles (max 1500)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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 clearly describes the return format (list of dicts with specific fields) which is valuable, but doesn't mention rate limits, authentication requirements, data freshness, or error conditions. The description adds meaningful output information but leaves other behavioral aspects unspecified.

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 perfectly concise with two sentences: one stating the purpose and one detailing the return format. Every word earns its place, and the most important information (what the tool does) is front-loaded. No wasted words or redundancy.

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 that an output schema exists (though not shown here), the description doesn't need to explain return values, and it does provide the return structure anyway. With 100% schema coverage and clear purpose, it's mostly complete for a data retrieval tool, though additional behavioral context (like rate limits) would be helpful since no annotations are provided.

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?

With 100% schema description coverage, the input schema already fully documents all three parameters. The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline expectation but doesn't provide additional semantic context about how parameters interact or affect results.

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 specific action ('Get OHLCV candlestick data') and resource ('for a symbol'), distinguishing it from sibling tools like get_ticker or get_recent_trades that provide different market data. It precisely identifies the type of financial data being retrieved.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for retrieving historical price data in candlestick format, but provides no explicit guidance on when to choose this tool over alternatives like get_ticker (current price) or get_recent_trades (trade-level data). It doesn't mention prerequisites or exclusions.

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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