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resolve_date_range

Parses natural language date expressions (like 'this week' or 'last 30 days') into standardized date ranges, ensuring consistent timing across AI analyses.

Instructions

【推荐优先调用】将自然语言日期表达式解析为标准日期范围

为什么需要这个工具? 用户经常使用"本周"、"最近7天"等自然语言表达日期,但 AI 模型自己计算日期 可能导致不一致的结果。此工具在服务器端使用精确的当前时间计算,确保所有 AI 模型获得一致的日期范围。

推荐使用流程:

  1. 用户说"分析AI本周的情感倾向"

  2. AI 调用 resolve_date_range("本周") → 获取精确日期范围

  3. AI 调用 analyze_sentiment(topic="ai", date_range=上一步返回的date_range)

Args: expression: 自然语言日期表达式,支持: - 单日: "今天", "昨天", "today", "yesterday" - 周: "本周", "上周", "this week", "last week" - 月: "本月", "上月", "this month", "last month" - 最近N天: "最近7天", "最近30天", "last 7 days", "last 30 days" - 动态: "最近5天", "last 10 days"(任意天数)

Returns: JSON格式的日期范围,可直接用于其他工具的 date_range 参数: { "success": true, "expression": "本周", "date_range": { "start": "2025-11-18", "end": "2025-11-26" }, "current_date": "2025-11-26", "description": "本周(周一到周日,11-18 至 11-26)" }

Examples: 用户:"分析AI本周的情感倾向" AI调用步骤: 1. resolve_date_range("本周") → {"date_range": {"start": "2025-11-18", "end": "2025-11-26"}, ...} 2. analyze_sentiment(topic="ai", date_range={"start": "2025-11-18", "end": "2025-11-26"})

用户:"看看最近7天的特斯拉新闻"
AI调用步骤:
1. resolve_date_range("最近7天")
   → {"date_range": {"start": "2025-11-20", "end": "2025-11-26"}, ...}
2. search_news(query="特斯拉", date_range={"start": "2025-11-20", "end": "2025-11-26"})

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
expressionYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

No annotations are provided, but the description fully discloses the tool's behavior: it uses server-side current time for consistent results, returns a JSON date range, and explains the return fields including success, expression, date_range, current_date, and description.

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, bullet points, and examples. It is front-loaded with the recommendation. However, it is slightly verbose with repeated examples; could be tightened while retaining clarity.

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

Completeness5/5

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

For a tool with one parameter and an output schema, the description is exceptionally complete. It explains why the tool exists, when to use it, how to use it, and what it returns. No gaps remain.

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 only parameter 'expression' has 0% schema description coverage, but the description compensates by listing supported formats (single day, week, month, last N days) with examples. This adds far more meaning than the bare 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 the tool's purpose: parsing natural language date expressions into standard date ranges. It distinguishes itself from sibling tools by being a utility date resolver, not a news or analysis tool.

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

Usage Guidelines5/5

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

The description explicitly recommends this tool as a 'preferred first call' and provides a clear step-by-step workflow with examples showing how to integrate it with other tools like analyze_sentiment and search_news.

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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