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es3154

Turf-MCP

by es3154

coordinate_mutation_truncate

Reduce coordinate precision in GeoJSON geometries to simplify data or remove Z-values using Turf.js truncate method.

Instructions

截断 GeoJSON 几何图形的坐标精度。

该函数使用 Turf.js 库的 truncate 方法,减少 GeoJSON 几何图形坐标的小数精度, 并可选择移除 Z 坐标值。

Args: geojson: GeoJSON 特征或几何图形 - 类型: str (JSON 字符串格式的 GeoJSON) - 格式: 任何有效的 GeoJSON 对象 - 坐标系: WGS84 (经度在前,纬度在后) - 示例: '{"type": "Point", "coordinates": [70.46923055566859, 58.11088890802906, 1508]}'

options: 可选参数配置
    - 类型: str (JSON 字符串) 或 None
    - 可选字段:
        - precision: 坐标小数精度 (默认: 6)
        - coordinates: 最大坐标维度数 (主要用于移除 Z 坐标) (默认: 3)
        - mutate: 是否允许修改输入 GeoJSON (默认: false)
    - 示例: '{"precision": 3, "coordinates": 2, "mutate": false}'

Returns: str: JSON 字符串格式的精度截断后的 GeoJSON 特征 - 类型: GeoJSON Feature - 格式: 坐标精度截断后的 GeoJSON - 示例: '{"type": "Feature", "geometry": {"type": "Point", "coordinates": [70.469, 58.111]}}'

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 >>> point = '{"type": "Point", "coordinates": [70.46923055566859, 58.11088890802906, 1508]}' >>> options = '{"precision": 3, "coordinates": 2}' >>> result = asyncio.run(truncate(point, options)) >>> print(result) '{"type": "Feature", "geometry": {"type": "Point", "coordinates": [70.469, 58.111]}}'

Notes: - 输入参数 geojson 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) - 通过设置 coordinates 为 2 可以移除 Z 坐标值 - 依赖于 Turf.js 库和 Node.js 环境

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
geojsonYes
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 does an excellent job describing key behaviors: it explains the underlying Turf.js implementation, specifies coordinate system (WGS84 with longitude-first ordering), describes the mutation option ('mutate' parameter), documents error conditions (JavaScript execution failure, timeout, input format errors), and notes JSON string requirements. The only minor gap is lack of explicit rate limit or performance characteristics.

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 front-loaded purpose statement. While comprehensive, it's appropriately sized for a tool with complex parameter semantics and no schema descriptions. Some sections could be slightly more concise, but every sentence adds value given the context.

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 (coordinate transformation with multiple options), zero schema description coverage, no annotations, but with output schema provided, the description is remarkably complete. It covers purpose, parameters with examples, return format with example, error conditions, implementation details, dependencies, and important notes about coordinate ordering and JSON requirements. The output schema handles return type documentation, allowing the description to focus on semantic context.

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?

With 0% schema description coverage (schema only shows parameter names and types without descriptions), the description fully compensates by providing comprehensive parameter documentation. It details both parameters: 'geojson' with format requirements, coordinate system, and examples; 'options' with all optional fields, defaults, and examples. This adds substantial value 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 clearly states the tool's purpose: '截断 GeoJSON 几何图形的坐标精度' (truncate coordinate precision of GeoJSON geometries). It specifies the verb ('截断' - truncate), the resource ('GeoJSON 几何图形' - GeoJSON geometries), and distinguishes from siblings by focusing on coordinate precision truncation rather than other coordinate mutations like 'clean_coords', 'flip', 'rewind', or 'round_number'.

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 reducing coordinate decimal precision and optionally removing Z coordinates in GeoJSON geometries. It mentions the Turf.js library dependency and Node.js environment, which are important prerequisites. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools for different coordinate manipulation needs.

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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