spread_correlation

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 states the tool computes autocorrelation but does not confirm that it is read-only, nor does it describe output format, error handling, or limitations. The agent has little information about side effects or requirements.

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 two sentences, concise and front-loaded with the core purpose. It avoids extraneous information, earning a high score for efficient communication.

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?

Without an output schema, the description should clarify what the tool returns, but it does not. It also lacks prerequisites, file size limits, or error conditions. For a detection tool, this gap in completeness is significant.

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 coverage is 100% with descriptions for all three parameters. The description does not add significant detail beyond the schema, so it meets the baseline expectation but does not enhance parameter understanding.

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 states it computes autocorrelation of pixel values to find periodic embedding patterns, with a clear verb and resource. It distinguishes from siblings like spread_dft_analysis by focusing on autocorrelation, but could be more explicit about when to use this specific method.

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 use for detecting spread spectrum and watermarking methods, but offers no explicit when-to-use or when-not-to-use guidance. No alternatives are mentioned, leaving the agent to infer from context.

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