list_datasets

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

The description details search semantics (case-insensitive substring across service/layer/field names), source filters, and return structure including the 'broken' field. Since no annotations are provided, this thorough explanation covers the behavior well, but it could explicitly state read-only or note pagination/error behavior.

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 well-structured with a one-sentence purpose, a usage note with examples, and labeled Args/Returns sections. Every sentence contributes information without redundancy.

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 discovery tool with three optional parameters and a structured output, the description covers all necessary semantics, workflow context, and return shape. It's self-sufficient even without the output schema.

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 fully compensates by explaining each parameter: keyword's substring matching and omission behavior, source's allowed values, and limit's default. This adds meaning beyond the bare schema.

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 uses a specific verb 'Search' and names the resource 'Charlotte/Mecklenburg ArcGIS endpoint registry'. It clearly distinguishes itself from sibling spatial/attribute tools by positioning itself as a precursor step to discover the correct layer.

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?

It provides explicit guidance to use this tool 'before calling a spatial/attribute tool', with concrete examples. This establishes proper workflow context, though it doesn't name specific alternative tools or include 'when not to use' exclusion.

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