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es3154

Turf-MCP

by es3154

transformation_clone

Create deep copies of GeoJSON objects to preserve all geometry, properties, and non-standard attributes for safe data manipulation in geospatial workflows.

Instructions

创建 GeoJSON 对象的完整副本。

此功能创建输入 GeoJSON 对象的深拷贝,包括所有属性和几何信息。

Args: geojson: GeoJSON 对象 - 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": "Feature", "geometry": {"type": "LineString", "coordinates": [[-74, 40], [-78, 42], [-82, 35]]}, "properties": {"color": "red"}}'

Returns: str: JSON 字符串格式的克隆后的 GeoJSON 对象 - 类型: 与输入相同的 GeoJSON 类型 - 格式: 与输入相同的格式,包含所有属性和外成员 - 示例: '{"type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-74, 40], [-78, 42], [-82, 35]]}, "properties": {"color": "red"}}'

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 >>> geojson = '{"type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-74, 40], [-78, 42], [-82, 35]]}, "properties": {"color": "red"}}' >>> result = asyncio.run(clone(geojson)) >>> print(result) '{"type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-74, 40], [-78, 42], [-82, 35]]}, "properties": {"color": "red"}}'

Notes: - 输入参数 geojson 必须是有效的 JSON 字符串 - 坐标顺序为 [经度, 纬度] (WGS84 坐标系) - 此方法比 JSON.parse + JSON.stringify 更快 - 包含所有可能的"外成员"(非标准属性) - 依赖于 Turf.js 库和 Node.js 环境

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
geojsonYes

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's a deep copy operation (non-destructive), includes all properties and '外成员' (foreign members), uses WGS84 coordinate system, is faster than JSON.parse+JSON.stringify, depends on Turf.js and Node.js, and raises exceptions for failures. This covers most aspects an agent needs, though it doesn't detail performance limits or auth requirements.

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's verbose with redundant information (e.g., the example repeats the input in the output). Some details like '依赖于 Turf.js 库和 Node.js 环境' could be more concise. It's front-loaded with the core purpose, 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 (a single-parameter transformation), no annotations, 0% schema coverage, but with an output schema (implied by 'Returns' section), the description is highly complete. It covers purpose, usage, parameters, behavior, returns, errors, examples, and technical notes, leaving no gaps for an agent to operate 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 extensive parameter semantics: geojson must be a JSON string, valid GeoJSON object, in WGS84 with longitude-first coordinate order, and includes a detailed example. This adds significant meaning beyond the bare schema, fully documenting the single parameter.

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: '创建 GeoJSON 对象的完整副本' (creates a complete copy of a GeoJSON object). It specifies it's a deep copy including all properties and geometry, which distinguishes it from sibling transformation tools that modify geometry (like buffer, simplify, union) rather than copying.

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 implies usage context through the 'Args' and 'Notes' sections, explaining the input must be a valid JSON string with specific coordinate order. However, it doesn't explicitly state when to use this tool versus alternatives (e.g., when needing a copy vs. direct manipulation), nor does it mention sibling tools for comparison.

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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