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es3154

Turf-MCP

by es3154

measurement_rhumbBearing

Calculate the rhumb line bearing between two geographic points to determine direction along a constant compass course for navigation and mapping applications.

Instructions

计算两点之间的恒向线方位角。

该函数使用 Turf.js 库的 rhumbBearing 方法,计算从第一个点到第二个点的恒向线方位角。

Args: point1: 起点 GeoJSON Point 特征或几何图形 - 类型: str (JSON 字符串格式的 GeoJSON) - 格式: 必须符合 GeoJSON Point 规范 - 坐标系: WGS84 (经度在前,纬度在后) - 示例: '{"type": "Point", "coordinates": [-75.343, 39.984]}'

point2: 终点 GeoJSON Point 特征或几何图形
    - 类型: str (JSON 字符串格式的 GeoJSON)
    - 格式: 必须符合 GeoJSON Point 规范
    - 坐标系: WGS84 (经度在前,纬度在后)
    - 示例: '{"type": "Point", "coordinates": [-75.534, 39.123]}'

options: 可选参数配置
    - 类型: str (JSON 字符串) 或 None
    - 可选字段:
        - final: 是否计算最终方位角 (默认: False)
    - 示例: '{"final": true}'

Returns: str: JSON 字符串格式的方位角结果对象 - 类型: 包含 value 和 units 的对象 - 格式: {"value": 方位角数值, "units": "degrees"} - 示例: '{"value": 45.5, "units": "degrees"}'

Raises: Exception: 当 JavaScript 执行失败、超时或输入数据格式错误时抛出异常

Example: >>> import asyncio >>> point1 = '{"type": "Point", "coordinates": [-75.343, 39.984]}' >>> point2 = '{"type": "Point", "coordinates": [-75.534, 39.123]}' >>> options = '{"final": true}' >>> result = asyncio.run(rhumbBearing(point1, point2, options)) >>> print(result) '45.5'

Notes: - 输入参数 point1、point2 和 options 必须是有效的 JSON 字符串 - 坐标顺序为 [经度, 纬度] (WGS84 坐标系) - 恒向线方位角是沿着恒向线(等角航线)的方位角 - 依赖于 Turf.js 库和 Node.js 环境

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
point1Yes
point2Yes
optionsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it calculates a rhumb bearing, returns a JSON object with value and units, requires valid JSON strings as input, uses WGS84 coordinates, and raises exceptions on failures. It also notes dependencies (Turf.js, Node.js) and coordinate order. This covers most behavioral aspects well, though it could mention performance or rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

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

The description is well-structured with sections (Args, Returns, Raises, Example, Notes), but it is verbose and includes redundant information (e.g., repeating JSON string format details). Some sentences could be more concise, and the front-loading is moderate—the core purpose is clear early, but details are extensive. It earns its place but could be tighter.

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?

Given the tool's complexity (geospatial calculation), no annotations, 0% schema coverage, but with an output schema (implied by Returns section), the description is highly complete. It covers purpose, usage, parameters, return values, errors, examples, and notes, providing all necessary context for an AI agent to use the tool correctly. The output schema is effectively described in the Returns section.

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 must fully compensate. It does so excellently: it explains all three parameters (point1, point2, options) in detail, including types, formats, coordinate systems, examples, and optional fields. This adds significant meaning beyond the bare schema, making the parameters fully understandable.

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: '计算两点之间的恒向线方位角' (calculates the rhumb bearing between two points). It specifies the exact operation (rhumb bearing calculation), distinguishes it from siblings like 'measurement_bearing' (which likely calculates great-circle bearing), and mentions the underlying Turf.js method. This is specific and differentiated.

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 provides clear context for when to use this tool: for calculating rhumb bearings (constant bearing) between two points, as opposed to great-circle bearings. It mentions the dependency on Turf.js and Node.js, which is useful. However, it does not explicitly state when not to use it or name alternatives (e.g., 'measurement_bearing'), though the distinction is implied by the tool name and description.

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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