binance_compare_spot_futures_price

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

With no annotations, the description carries the full transparency burden. It discloses read-only analysis behavior, includes a detailed output example, and warns about volatility, costs, liquidity, and timeliness. It does not cover API-level details like authentication or rate limits, but for a simple comparison tool, it is sufficiently transparent.

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 overly long and cluttered with emojis, repeated risk warnings, and an extensive output example. The core information is buried under promotional formatting, and several sentences are redundant (e.g., multiple risk reminders repeated). It is not front-loaded, making it less efficient 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?

Despite being verbose, the description is contextually complete: it explains the purpose, use cases, warnings, and includes a detailed output example that compensates for the lack of an output schema. For a single-parameter read-only tool, it covers all necessary aspects an agent needs to select and invoke it.

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 input schema already provides 100% coverage for the only parameter 'symbol' with examples and a description. The tool description adds no new semantic meaning beyond confirming that the symbol is used to compare spot and perpetual futures prices, so the baseline score of 3 applies.

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 opens with '现货期货价差分析 - 套利机会智能识别' which clearly states it analyzes spot-futures price differences and identifies arbitrage opportunities. This verb+resource combination distinguishes it from sibling tools like binance_spot_price and binance_futures_price, which only fetch single-market prices.

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 '适用场景' section explicitly lists use cases: professional arbitrage traders, sentiment analysis, quant strategy evaluation, and risk monitoring. This provides clear context for when to use the tool, though it does not explicitly mention alternatives with when-not-to-use guidance.

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