get_field_info

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

The description states the tool does not modify data, which is critical behavioral transparency given no annotations are provided. It also describes the output fields, but could mention error conditions or performance implications.

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 concise sentences: the first states the action and method, the second provides usage context and return details. No superfluous information.

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 simple read-only tool with one parameter and an output schema, the description covers purpose, usage context, and behavior completely. It explains what it returns and that it is non-destructive.

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 description does not add meaning beyond the input schema's detailed description of the dataset parameter (absolute path, PathGuard constraint). With 0% schema description coverage indicated, but the schema itself is informative, a baseline 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 clearly states the tool lists field metadata for feature classes or tables using ArcPy ListFields, specifying what it returns (names, types, lengths, aliases, nullable status). This distinguishes it from sibling tools that modify data, such as add_field or delete_field.

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 explicitly says to use this tool before calculating, deleting, joining, or exporting attributes, providing clear context for when to inspect schema. While it does not list alternatives, the sibling tools' write operations make the use case clear.

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