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es3154

Turf-MCP

by es3154

grid_triangleGrid

Generate triangular mesh grids within bounding boxes for spatial analysis and surface modeling. Specify bounding box coordinates and cell size to create GeoJSON triangle features.

Instructions

在边界框内生成三角形网格。

此功能在指定的边界框内创建三角形网格,用于空间分析和表面建模。

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: 三角形每条边的长度
    - 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: 传递给所有三角形的属性对象
        - mask: 用于裁剪网格的多边形特征
    - 示例: '{"units": "miles", "properties": {"type": "triangle"}}'

Returns: str: JSON 字符串格式的 GeoJSON FeatureCollection - 类型: GeoJSON FeatureCollection with Polygon features - 格式: {"type": "FeatureCollection", "features": [{"type": "Feature", "geometry": {"type": "Polygon", "coordinates": [...]}, "properties": {...}}, ...]} - 示例: '{"type": "FeatureCollection", "features": [{"type": "Feature", "geometry": {"type": "Polygon", "coordinates": [...]}, "properties": {"type": "triangle"}}, ...]}'

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(triangleGrid(bbox, 50.0, '{"units": "miles"}')) >>> print(result) '{"type": "FeatureCollection", "features": [{"type": "Feature", "geometry": {"type": "Polygon", "coordinates": [...]}, "properties": {"type": "triangle"}}, ...]}'

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) - 三角形网格提供灵活的空间划分,常用于表面建模 - 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 the tool's behavior: it generates a triangular grid, returns a GeoJSON FeatureCollection, and includes error handling details (e.g., raises exceptions for JavaScript execution failures or input format errors). It also notes dependencies on Turf.js and Node.js, adding useful context beyond basic functionality.

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 structured with sections like 'Args', 'Returns', 'Raises', 'Example', and 'Notes', which aids readability. However, it includes some redundant information (e.g., repeating JSON format details) and could be more front-loaded. The length is appropriate for the complexity, but not all sentences are strictly necessary, reducing efficiency.

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?

The description is highly complete for a tool with no annotations and an output schema. It covers purpose, parameters, return values (including format and example), error handling, dependencies, and usage notes (e.g., coordinate order, JSON string requirements). Given the complexity and lack of structured fields, it provides all necessary context for an agent to invoke the tool correctly.

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?

Given a schema description coverage of 0%, the description compensates fully by providing detailed parameter semantics. It explains each parameter ('bbox', 'cell_size', 'options') with types, formats, examples, and optional fields (e.g., 'units', 'properties', 'mask'). This adds significant value beyond the minimal input schema, ensuring the agent understands how to use the parameters correctly.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: '在边界框内生成三角形网格' (generate a triangular grid within a bounding box). It specifies the action ('生成' - generate) and resource ('三角形网格' - triangular grid), making it distinct from siblings like 'grid_hexGrid' or 'grid_squareGrid'. However, it doesn't explicitly differentiate from all siblings, such as 'grid_pointGrid', which might also involve spatial grids but with different geometries.

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

Usage Guidelines3/5

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

The description implies usage for spatial analysis and surface modeling, as noted in '用于空间分析和表面建模'. It provides context but lacks explicit guidance on when to choose this tool over alternatives like 'grid_hexGrid' or 'grid_squareGrid'. No exclusions or prerequisites are mentioned, leaving the agent to infer based on the tool's purpose.

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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