joins_pointsWithinPolygon

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

With no annotations provided, the description carries full responsibility for behavioral disclosure. It thoroughly describes: the exact return format (GeoJSON FeatureCollection), edge case behavior (points on boundaries may be treated as inside or outside depending on implementation), dependencies (Turf.js and Node.js), error conditions (JavaScript execution failures, timeouts, malformed input), and coordinate system requirements (WGS84 with [longitude, latitude] order). This provides comprehensive behavioral context beyond basic functionality.

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 functionality. While comprehensive, it's appropriately sized for a complex spatial operation with many implementation details. Some redundancy exists (e.g., repeating GeoJSON format details), but overall it's efficient given the information density required.

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 (spatial join operation), lack of annotations, and 0% schema coverage, the description provides exceptional completeness. It covers: purpose, parameters with detailed semantics, return format (though output schema exists), error conditions, dependencies, coordinate system, edge cases, and a working example. This fully compensates for the minimal structured metadata available.

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 'points' and 'polygons' as strings), the description fully compensates by providing detailed parameter documentation. It specifies: exact GeoJSON types required (FeatureCollection with Point features vs Polygon/MultiPolygon features), JSON string format requirements, coordinate order, and concrete examples. This adds substantial value beyond the minimal schema information.

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: '查找多边形内部的点' (find points within polygons). It specifies the exact operation (identifying point features inside polygon features) and distinguishes itself from siblings like 'booleans_booleanPointInPolygon' by performing a spatial join rather than a boolean test, returning actual point features instead of a true/false result.

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: when you need to filter points based on polygon containment. It implicitly distinguishes from siblings by focusing on feature extraction rather than boolean evaluation or other spatial operations. However, it doesn't explicitly mention when NOT to use it or name specific alternatives among the many sibling tools.

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