kvant-binance-mcp

futures_liquidity_check

Check liquidity for a trading pair by analyzing spread percentage, order book depth near mid price, and volume.

Instructions

Check liquidity for a symbol — spread %, depth near mid price, and volume

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "symbol": "<symbol>"
    },
    "name": "futures_liquidity_check"
  }
}

Input Schema

Name	Required	Description	Default
`symbol`	Yes	Trading pair
`depthLimit`	No	Order book depth to analyze (default 100)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jul 4, 2026

TDQS

B3.3/5.0

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. It implies a read-only check but does not explicitly state whether it is non-destructive, whether it requires any permissions, or any other behavioral traits like rate limits. The description lacks behavioral context beyond the basic function.

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 sentence of 13 words, front-loaded with the key verb 'Check'. It is concise with no wasted words, every part earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and no annotations, the description is adequate but not rich. It explains the tool's purpose and output metrics, but does not detail the output structure, units (e.g., spread percentage vs absolute), or any edge cases. For a liquidity check tool, an agent might need more context about the return format.

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?

Input schema coverage is 100% with clear parameter descriptions ('Trading pair', 'Order book depth to analyze (default 100)'). The description adds value by explaining what the tool returns (spread %, depth, volume) but does not add further meaning to the parameters themselves. Baseline 3 is appropriate given schema coverage.

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 checks liquidity for a symbol and specifies the metrics returned: spread %, depth near mid price, and volume. It uses a specific verb ('Check') and resource ('liquidity'), and the details differentiate it from sibling tools like futures_order_book or futures_ticker_24hr.

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, nor does it mention scenarios or exclusions. There is no explicit 'when-to-use' or 'when-not-to-use' information.

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