knn_weights

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

No annotations are provided, so the description must fully disclose behavior. It only lists parameters and a high-level purpose, omitting details about error handling, side effects (e.g., in-memory object creation), or output format beyond the schema.

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 short and uses a bullet list for parameters, making it scannable. Every sentence is functional, but it could be slightly more informative without adding length.

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 existence of sibling tools and an output schema, the description fails to differentiate when to use knn_weights vs. other weight creation tools. It does not mention that the output is a PySAL W object, nor does it cover edge cases or provide usage context.

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?

With 0% schema coverage, the description adds brief explanations for all three parameters (data_path, k, id_field), clarifying their roles and types. However, it lacks constraints (e.g., k must be positive integer, id_field must be unique) and typical usage examples.

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 'Create a k-nearest neighbors spatial weights (W) object from point data', which is a specific verb+resource combination. It distinguishes from siblings like distance_band_weights and build_and_transform_weights.

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 such as distance_band_weights. There is no mention of prerequisites, limitations, or 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.