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drasticstatic

hummingbot-mcp

by drasticstatic
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

get_market_data

Retrieve real-time market data from any exchange: prices, OHLCV candles, perpetual funding rates, or order book snapshots and queries.

Instructions

Get market data: prices, candles, funding rates, or order book data.

Data Types:
- prices: Get latest prices for multiple trading pairs
- candles: Get OHLCV candle data for a trading pair
- funding_rate: Get perpetual funding rate (connector must have _perpetual)
- order_book: Get order book snapshot or queries

Args:
    data_type: Type of market data to retrieve ('prices', 'candles', 'funding_rate', 'order_book')
    connector_name: Exchange connector name (e.g., 'binance', 'binance_perpetual')
    trading_pairs: List of trading pairs (required for 'prices', e.g., ['BTC-USDT', 'ETH-USD'])
    trading_pair: Single trading pair (required for 'candles', 'funding_rate', 'order_book')
    interval: Candle interval for 'candles' (default: '1h'). Options: '1m', '5m', '15m', '30m', '1h', '4h', '1d'.
    days: Number of days of historical data for 'candles' (default: 30).
    query_type: Order book query type for 'order_book' (default: 'snapshot'). Options: 'snapshot',
        'volume_for_price', 'price_for_volume', 'quote_volume_for_price', 'price_for_quote_volume'.
    query_value: Value for order book queries (required if query_type is not 'snapshot').
    is_buy: Side for order book queries (default: True for buy side).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
data_typeYes
connector_nameYes
trading_pairsNo
trading_pairNo
intervalNo1h
daysNo
query_typeNo
query_valueNo
is_buyNo

Tool Definition Quality

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It discloses that funding_rate requires a '_perpetual' connector suffix and details order book query behavior. No destructive actions are mentioned, which is consistent with a read-only tool. 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.

Conciseness4/5

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

The description is well-structured with sections and bullet points, front-loading the purpose. However, it is somewhat verbose, especially listing all order book query types. Could be slightly more concise, but still effective.

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 the complexity of 9 parameters and no output schema, the description covers parameter dependencies and usage context comprehensively. It does not explain return values, but that is acceptable without an output schema. Overall, it provides sufficient context for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description fully compensates. It explains each parameter, including conditional requirements (e.g., trading_pairs for 'prices', trading_pair for others), default values, and options (interval, query_type). This adds significant meaning 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 'Get market data: prices, candles, funding rates, or order book data,' specifying the verb and resource. It distinguishes itself from sibling tools like explore_dex_pools and get_portfolio_overview by focusing on centralized exchange market data.

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 outlines which data type to use and the required parameters for each, but does not explicitly contrast with sibling tools or state when not to use it. The information is sufficient for an agent to select the correct data type.

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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