normal_cdf

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

No annotations are provided, so the description must fully disclose behavioral traits. It only states what the tool calculates, without mentioning computational details (e.g., precision, handling of edge cases like extreme values), performance characteristics, or error handling. For a mathematical tool with no annotation coverage, this lack of behavioral context is a significant gap.

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 extremely concise and front-loaded, consisting of a single, direct sentence that states the core functionality. There is no wasted verbiage or redundant information, making it efficient for quick understanding by an AI agent.

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 complexity (a mathematical function with 3 parameters), lack of annotations, no output schema, and low schema description coverage, the description is incomplete. It does not cover parameter semantics, return values, error conditions, or usage examples. For a tool that performs calculations, more context is needed to ensure reliable agent invocation.

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 input schema has 3 parameters with 0% description coverage, meaning the schema provides no semantic information. The description does not explain the parameters (x, mean, std), their roles, or typical values. However, since the tool's name and description imply a standard statistical function, an agent might infer parameter meanings from common knowledge, but this is not explicitly stated. The baseline score reflects this partial compensation.

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: 'Calculate the cumulative distribution function (CDF) of the normal distribution.' It specifies the verb ('calculate'), resource ('CDF'), and distribution type ('normal'), making the intent unambiguous. However, it does not explicitly differentiate this tool from other statistical or distribution-related siblings (e.g., 'normal_pdf', 'exponential_cdf'), which prevents a perfect score.

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 minimal usage guidance. It mentions the domain ('probability') and category ('general'), which implies context but does not specify when to use this tool versus alternatives (e.g., 'normal_pdf' for probability density or other CDF tools). There is no explicit advice on prerequisites, limitations, or typical scenarios, leaving the agent with insufficient direction.

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