random_randomPosition

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

With no annotations provided, the description carries full burden and does well. It discloses that the tool returns coordinates in [lng, lat] format, uses WGS84 coordinate system, has a default global bounding box, and depends on Turf.js and Node.js. It also mentions potential exceptions for JavaScript execution failures, timeouts, or input format errors. The only gap is lack of information about randomness characteristics or performance.

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-loaded with the core purpose. However, it includes some implementation details (Turf.js, Node.js) that may not be essential for tool selection, and the Python example might be overly specific for an MCP context. Overall efficient but could be slightly more concise.

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 tool with no annotations but with output schema, the description is remarkably complete. It covers purpose, parameter semantics, return format, error conditions, examples, and implementation dependencies. The output schema exists, so the description appropriately focuses on explaining the coordinate format rather than duplicating schema information. Nothing essential appears missing.

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?

Schema description coverage is 0%, so the description must fully compensate - which it does excellently. It provides detailed semantics for the single parameter 'bbox': format requirements (JSON string array [minX, minY, maxX, maxY]), default value (global [-180, -90, 180, 90]), examples, and validation rules (must be valid JSON or None). This goes far beyond what the minimal schema provides.

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: '生成随机的地理坐标位置' (generate random geographic coordinate positions). It specifies the verb ('生成随机' - generate random) and resource ('地理坐标位置' - geographic coordinate positions), and distinguishes itself from siblings like random_randomPoint or random_randomPolygon by focusing specifically on coordinate generation rather than geometric features.

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 about when to use this tool: when needing random geographic coordinates within a bounding box. It mentions the default global range and the coordinate system (WGS84). However, it doesn't explicitly contrast with alternatives like random_randomPoint (which generates random point features rather than raw coordinates) or specify when not to use it.

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