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es3154

Turf-MCP

by es3154

grid_pointGrid

Generate regularly spaced point grids within specified geographic bounding boxes for spatial sampling and interpolation applications in geospatial analysis.

Instructions

在边界框内生成点网格。

此功能在指定的边界框内创建规则分布的点网格,用于空间采样和插值。

Args: bbox: 边界框数组 - 类型: str (JSON 字符串格式的数组) - 格式: [minX, minY, maxX, maxY] - 示例: '[-180, -90, 180, 90]'

cell_size: 网格单元大小
    - 类型: float
    - 描述: 点之间的间距
    - 示例: 50.0

options: 可选参数配置
    - 类型: str (JSON 字符串) 或 None
    - 可选字段:
        - units: 距离单位 (默认: 'kilometers')
            - 有效值: 'miles', 'nauticalmiles', 'kilometers', 'meters', 'yards', 'feet', 'inches'
        - properties: 传递给所有点的属性对象
        - mask: 用于裁剪网格的多边形特征
    - 示例: '{"units": "miles", "properties": {"type": "grid_point"}}'

Returns: str: JSON 字符串格式的 GeoJSON FeatureCollection - 类型: GeoJSON FeatureCollection with Point features - 格式: {"type": "FeatureCollection", "features": [{"type": "Feature", "geometry": {"type": "Point", "coordinates": [lng, lat]}, "properties": {...}}, ...]} - 示例: '{"type": "FeatureCollection", "features": [{"type": "Feature", "geometry": {"type": "Point", "coordinates": [-75.343, 39.984]}, "properties": {"type": "grid_point"}}, ...]}'

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 >>> bbox = '[-180, -90, 180, 90]' >>> result = asyncio.run(pointGrid(bbox, 50.0, '{"units": "miles"}')) >>> print(result) '{"type": "FeatureCollection", "features": [{"type": "Feature", "geometry": {"type": "Point", "coordinates": [-75.343, 39.984]}, "properties": {"type": "grid_point"}}, ...]}'

Notes: - 输入参数 bbox 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) - 点网格提供规则的空间采样点,常用于插值和统计 - 依赖于 Turf.js 库和 Node.js 环境

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bboxYes
cell_sizeYes
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 key behaviors: the tool creates a rule-distributed point grid, returns a GeoJSON FeatureCollection, depends on Turf.js and Node.js, and raises exceptions for failures. It also notes input requirements (JSON strings, coordinate order) and typical use cases, though it lacks details on 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.

Conciseness4/5

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

The description is well-structured with clear sections (Args, Returns, Raises, Example, Notes) and is appropriately sized for the tool's complexity. It is front-loaded with the core purpose, but some sections (like the detailed parameter explanations) are lengthy, though necessary given the lack of schema descriptions.

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 (3 parameters, spatial operations) and the presence of an output schema (implied by the detailed Returns section), the description is highly complete. It covers purpose, parameters, return format, errors, examples, dependencies, and usage notes, providing all necessary context for an AI agent to use the tool effectively.

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 provides detailed semantics for all three parameters (bbox, cell_size, options), including types, formats, examples, optional fields with defaults, and valid values for enums (e.g., units). This adds significant value beyond the minimal input schema, ensuring parameters are well-understood.

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: '在边界框内生成点网格' (generate a point grid within a bounding box). It specifies the verb ('生成' - generate) and resource ('点网格' - point grid), and distinguishes it from sibling tools like grid_hexGrid, grid_squareGrid, and grid_triangleGrid by focusing on point generation rather than other grid types.

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: '用于空间采样和插值' (for spatial sampling and interpolation). It implies usage in spatial analysis scenarios but does not explicitly state when not to use it or name specific alternatives among siblings, though the sibling list includes other grid tools that serve different purposes.

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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