ln

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

With no annotations provided, the description carries full burden for behavioral disclosure. While it states what the tool does (calculates natural logarithm), it provides no information about error handling (e.g., what happens with negative or zero inputs), precision, return format, or computational characteristics. For a mathematical function with no annotation coverage, this represents significant gaps in behavioral context.

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 a single, efficient Chinese phrase that communicates the essential purpose without any wasted words. It's perfectly front-loaded with the core functionality and uses mathematical terminology that's standard and unambiguous in this context.

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 mathematical function with no annotations, no output schema, and 0% schema description coverage, the description is incomplete. While it clearly states the mathematical operation, it lacks crucial context about parameter constraints (positive numbers only), error conditions, return format, and precision. The agent would need to guess about important behavioral aspects.

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?

The schema description coverage is 0%, and the description provides no information about the single 'value' parameter. It doesn't explain what the parameter represents (the number to take the natural logarithm of), valid ranges (must be positive), or units. With low schema coverage and no parameter details in the description, this fails to compensate for the documentation gap.

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 '计算自然对数（以e为底）' (calculates natural logarithm with base e) clearly states the specific mathematical operation (natural logarithm) and distinguishes it from sibling tools like 'log' (generic logarithm) and 'log10' (base-10 logarithm). It uses precise terminology that identifies both the verb (calculate) and resource (natural logarithm).

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 implicitly provides excellent usage guidance by specifying '以e为底' (with base e), which clearly differentiates it from the 'log' and 'log10' sibling tools. This tells the agent exactly when to use this tool (for natural logarithms) versus alternatives (for other bases). The mathematical context makes the distinction self-evident.

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