transformation_difference

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden and does an excellent job. It discloses: the underlying library (Turf.js), execution environment (Node.js), error conditions (JavaScript failures, timeouts, format errors), mathematical behavior (first polygon minus second polygon), and output format details. The only minor gap is lack of explicit performance characteristics or rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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. Every sentence adds value, though the Python example (with asyncio) might be slightly verbose for a tool description. The Chinese/English mixing is efficient for the intended audience.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter geometric transformation tool with no annotations but with output schema, this description is exceptionally complete. It covers purpose, usage, parameters, return values, errors, examples, dependencies, and mathematical behavior. The output schema existence means the description doesn't need to exhaustively document return structure, but it still provides helpful format details.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage (schema only says 'featureCollection' is a string), the description fully compensates. It provides comprehensive parameter documentation: type (JSON string), format (GeoJSON FeatureCollection), coordinate system (WGS84), ordering (longitude first), validation requirements, and a complete example. 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.

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

The description clearly states the tool's purpose: '计算两个多边形的差异' (calculates the difference between two polygons). It specifies the exact operation (difference calculation using Turf.js), distinguishes it from siblings like 'transformation_intersect' or 'transformation_union' by focusing on geometric difference, and provides the mathematical definition ('返回第一个多边形中不在第二个多边形中的部分').

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

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 calculating geometric differences between two polygons. It specifies input requirements (GeoJSON FeatureCollection with two polygons) and coordinate system (WGS84). However, it doesn't explicitly mention when NOT to use it or name specific alternatives among the many sibling transformation tools, though the mathematical operation itself implies differentiation from union/intersect tools.

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