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drasticstatic

hummingbot-mcp

by drasticstatic
OverviewSchemaSourceRelated ServersDiscussionsAdmin
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

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.4

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description bears full burden. It discloses that the tool reads market data and specifies which parameters are required for each data type. It does not mention side effects or rate limits, but it is clear that the tool is read-only. The structure provides good insight into behavior.

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 clear headings and bullet points, front-loading the purpose. It is detailed but not overly verbose; every sentence adds value. Minor redundancy (e.g., repeating enum options) but overall efficient.

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 (9 params, no output schema), the description covers all data types and their necessary parameters. It does not describe return format or pagination, but for a read tool, this is adequate. It provides enough context for an agent to select and use the tool correctly.

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?

The description adds significant meaning beyond the schema: it explains which parameters are required for each data type, provides examples, and defines enumeration options (e.g., interval choices, query types). Since schema coverage is 0%, this fully compensates and is essential for correct invocation.

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 tool retrieves market data (prices, candles, funding rates, order book) and enumerates each data type with a specific verb (e.g., 'Get latest prices'). This is explicit and distinguishes the tool from siblings focused on other domains like DEX pools or portfolio overview.

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 explains what each data type does but does not provide when-to-use advice or comparison to alternatives (e.g., when to use this vs explore_dex_pools). It implies usage through the listed data types but lacks explicit guidelines on 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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