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es3154

Turf-MCP

by es3154

transformation_convex

Generate convex hull polygons from point sets using Turf.js to create the smallest convex shape containing all input points for spatial analysis.

Instructions

计算点集的凸包。

该函数使用 Turf.js 库的 convex 方法,从一组点生成凸包多边形。

Args: points: 点集 GeoJSON FeatureCollection - 类型: str (JSON 字符串格式的 GeoJSON) - 格式: 必须符合 GeoJSON FeatureCollection 规范,包含 Point 特征 - 坐标系: WGS84 (经度在前,纬度在后) - 示例: '{"type": "FeatureCollection", "features": [{"type": "Feature", "geometry": {"type": "Point", "coordinates": [10.195312, 43.755225]}}, ...]}'

Returns: str: JSON 字符串格式的凸包 GeoJSON Polygon 特征 - 类型: GeoJSON Feature with Polygon geometry - 格式: {"type": "Feature", "geometry": {"type": "Polygon", "coordinates": [...]}} - 示例: '{"type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[10.195312, 43.755225], [10.404052, 43.8424511], ...]]}}'

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 >>> points = '{"type": "FeatureCollection", "features": [{"type": "Feature", "geometry": {"type": "Point", "coordinates": [10.195312, 43.755225]}}]}' >>> result = asyncio.run(convex(points)) >>> print(result) '{"type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[10.195312, 43.755225], [10.404052, 43.8424511], ...]]}}'

Notes: - 输入参数 points 必须是有效的 JSON 字符串 - 坐标顺序为 [经度, 纬度] (WGS84 坐标系) - 凸包是包含所有点的最小凸多边形 - 依赖于 Turf.js 库和 Node.js 环境

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pointsYes

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. It discloses key behavioral traits: it uses Turf.js and Node.js, specifies input/output formats (GeoJSON strings), coordinate system (WGS84), and error conditions (exceptions for execution failures, timeouts, or bad data). However, it does not mention performance aspects like computational complexity or limitations on point count.

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 (Args, Returns, Raises, Example, Notes) and front-loaded purpose. It is appropriately sized but includes some redundancy (e.g., repeating coordinate order in multiple sections), slightly 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?

Given the complexity (geometric computation), no annotations, and an output schema exists, the description is complete. It covers purpose, input details, output format, errors, examples, and dependencies, providing sufficient context for an agent to invoke the tool correctly without needing the output schema explained.

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 the single parameter 'points': type (JSON string), format (GeoJSON FeatureCollection with Points), coordinate system (WGS84 with longitude-first), and an example. This adds substantial meaning beyond 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 starts with a clear, specific statement: '计算点集的凸包' (computes the convex hull of a point set). It explicitly names the resource (point set) and the verb (compute convex hull), and distinguishes from siblings by focusing on convex hull generation rather than other geometric transformations like 'transformation_concave' or 'transformation_union'.

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 through the definition of convex hull ('包含所有点的最小凸多边形') and notes on input format, but does not explicitly state when to use this tool versus alternatives like 'transformation_concave' for concave hulls or other geometric operations. It provides context but lacks explicit comparative guidance.

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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