Skip to main content
Glama
es3154

Turf-MCP

by es3154

joins_tag

Add polygon attributes to point features by performing spatial joins, enabling geographic data enrichment through location-based property transfers.

Instructions

为点特征添加多边形属性。

此功能将多边形特征的属性值关联到位于多边形内部的点特征上。

Args: points: 点特征集合 - 类型: str (JSON 字符串格式的 GeoJSON FeatureCollection) - 格式: FeatureCollection with Point features - 示例: '{"type": "FeatureCollection", "features": [{"type": "Feature", "geometry": {"type": "Point", "coordinates": [-75.343, 39.984]}}, ...]}'

polygons: 多边形特征集合
    - 类型: str (JSON 字符串格式的 GeoJSON FeatureCollection)
    - 格式: FeatureCollection with Polygon or MultiPolygon features
    - 示例: '{"type": "FeatureCollection", "features": [{"type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[125, -15], [113, -22], [154, -27], [144, -15], [125, -15]]], "properties": {"name": "Area A"}}, ...]}'

field: 源字段名
    - 类型: str
    - 描述: 多边形特征中要提取的属性字段名
    - 示例: 'name'

out_field: 输出字段名
    - 类型: str
    - 描述: 点特征中要创建的属性字段名
    - 示例: 'area_name'

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": {"area_name": "Area A"}}, ...]}'

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": [-75.343, 39.984]}}]}' >>> polygons = '{"type": "FeatureCollection", "features": [{"type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[125, -15], [113, -22], [154, -27], [144, -15], [125, -15]]], "properties": {"name": "Area A"}}]}' >>> result = asyncio.run(tag(points, polygons, 'name', 'area_name')) >>> print(result) '{"type": "FeatureCollection", "features": [{"type": "Feature", "geometry": {"type": "Point", "coordinates": [-75.343, 39.984]}, "properties": {"area_name": "Area A"}}, ...]}'

Notes: - 输入参数 points 和 polygons 必须是有效的 JSON 字符串 - 坐标顺序为 [经度, 纬度] (WGS84 坐标系) - 仅对位于多边形内部的点添加属性 - 如果一个点位于多个多边形内,将使用最后一个匹配的多边形属性 - 依赖于 Turf.js 库和 Node.js 环境

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pointsYes
polygonsYes
fieldYes
out_fieldYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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 and does so comprehensively. It explains: the spatial logic ('仅对位于多边形内部的点添加属性'), edge cases ('如果一个点位于多个多边形内,将使用最后一个匹配的多边形属性'), coordinate system requirements ('坐标顺序为 [经度, 纬度] (WGS84 坐标系)'), dependencies ('依赖于 Turf.js 库和 Node.js 环境'), input format requirements ('输入参数 points 和 polygons 必须是有效的 JSON 字符串'), and error conditions ('当 JavaScript 执行失败、超时或输入数据格式错误时抛出异常').

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-loads the core purpose. While comprehensive, it's appropriately sized for a complex spatial operation with 4 parameters and no annotations. Every section adds value, though the Example section is quite detailed.

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 (spatial join operation), lack of annotations, and 4 parameters with 0% schema coverage, the description provides complete context. It covers purpose, parameters, return values (with output schema), error conditions, examples, and important behavioral notes. The presence of an output schema helps, but the description still adds valuable context about the spatial logic and edge cases.

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, the description fully compensates by providing detailed parameter documentation. Each of the 4 parameters gets: name, type, format description, examples, and clear explanations of their purpose. The description adds substantial meaning beyond what the bare schema provides, including GeoJSON format specifications and field mapping logic.

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: '为点特征添加多边形属性' (add polygon attributes to point features) and '此功能将多边形特征的属性值关联到位于多边形内部的点特征上' (this function associates polygon feature attribute values to point features inside polygons). It specifies the exact operation (spatial join) and distinguishes it from sibling tools like 'joins_pointsWithinPolygon' by focusing on attribute transfer rather than just spatial containment.

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: when you need to transfer attributes from polygons to points based on spatial containment. It doesn't explicitly mention when NOT to use it or name specific alternatives, but the context is sufficiently clear given the sibling tool list includes 'joins_pointsWithinPolygon' which might serve a similar but distinct purpose.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/es3154/turf-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server