booleans_booleanEqual

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

With no annotations provided, the description carries full burden and adds significant behavioral context beyond basic functionality. It discloses: coordinate system requirements (WGS84), structural equality criteria, that properties don't affect equality, dependency on Turf.js/Node.js, and error conditions (JavaScript execution failures, timeouts, malformed data). This provides good transparency about how the tool behaves and its constraints.

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. While comprehensive, some Notes could be more concise (e.g., the Turf.js dependency might be implied by context). Overall, most sentences earn their place by adding value.

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?

Given the tool's complexity (geometric equality comparison with specific coordinate systems and dependencies), no annotations, and an output schema that only indicates a boolean result, the description provides excellent completeness. It covers purpose, parameters, returns, errors, examples, and implementation details, making it fully self-contained for an AI agent.

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 shows parameter names and types), the description fully compensates by providing detailed parameter documentation: data types (JSON strings), formats (valid GeoJSON objects), examples, and specific requirements ('必须是有效的 JSON 字符串'). This adds substantial meaning beyond the minimal input 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 with specific verb ('检查两个几何图形是否相等') and resource ('两个GeoJSON几何图形'), distinguishing it from sibling tools like 'booleans_booleanContains' or 'booleans_booleanWithin' which perform different geometric comparisons. The first sentence directly answers 'what does this tool do?'

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 implies usage through the Notes section (e.g., '坐标顺序为 [经度, 纬度]', '属性不同不影响几何相等性判断'), but doesn't explicitly state when to use this tool versus alternatives like 'booleans_booleanContains' for containment checks. There's no 'use this when...' or 'instead of...' guidance, leaving usage context implicit rather than explicit.

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