near_analysis

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

With no annotations, the description carries full behavioral disclosure. It explicitly states the tool mutates the input by adding/updating NEAR_FID and NEAR_DIST fields, requires confirm=true, and recommends using a copy. This fully informs an agent of the mutation risk and safety measures.

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?

Three concise sentences with no fluff. The first sentence states the core purpose, the second explains the mutation effect and prerequisite, and the third gives safety guidance. Highly efficient.

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 mutating tool with an output schema, the description covers all necessary context: what it does, what it changes, what input validation is needed, and best practices. Nothing critical is missing.

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 already contains good descriptions for all parameters (100% coverage). The tool description reiterates the mutation and confirm requirement but adds no new parameter-specific meaning beyond the schema. Per guidelines, baseline 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 starts with a clear verb ('Calculate nearest-neighbor distance') and specifies the resource ('each input feature'). It distinguishes from siblings like 'generate_near_table' by highlighting mutation and the confirm requirement.

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 advises using a copied working dataset when exposing to an LLM/automated agent and requires confirm=true. It does not explicitly name alternatives for non-mutating scenarios, but the advice is clear for this tool's mutation behavior.

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