np_correlate

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

No annotations exist, so the description carries the full burden. It only states the operation without disclosing behavioral traits such as boundary conditions, normalization behavior, or performance characteristics. For a signal processing tool, this is insufficient.

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 short phrase, which is concise and front-loaded with the core purpose. However, it could be slightly expanded to mention mode options or return value without losing conciseness.

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?

An output schema exists, so the description need not detail return values. The description is minimal and does not explain what the cross-correlation output represents or the effect of different mode values. For a tool with three parameters, it is adequate but not comprehensive.

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 covers 100% of parameters with descriptions. The tool description adds no additional context beyond what the schema provides; for example, it does not clarify that 'mode' affects output length. Baseline score of 3 is appropriate.

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 'Cross-correlation of two 1-dimensional sequences' uses a specific verb and resource, clearly indicating the operation on 1D sequences. It distinguishes from sibling 'np_corrcoef' (which computes correlation coefficients) by specifying cross-correlation.

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?

No guidance is provided on when to use this tool versus alternatives like np_corrcoef or np_cov. There is no mention of prerequisites, exclusions, or context for choosing this tool over siblings.

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