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

Annotations already declare readOnlyHint=true, so the safe read nature is established. The description adds useful behavioral context by specifying the output includes 'dataset/series counts, cadence notes, and terms links,' and clarifies the scope ('official statistical sources served by this data layer'). This goes beyond the annotation without contradicting it.

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 sentences, front-loaded with the main action and resource, followed by a concise list of output contents and a clear directive. No wasted words; every element earns its place.

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, parameterless list tool with a readOnly annotation, the description fully covers what the tool does, what it returns, and when to use it. No output schema is present, but the description provides enough detail about the returned information to set expectations.

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 is empty (no parameters), which gives a baseline of 4. The description does not need to explain parameter meaning since there are none; it correctly focuses on what the tool returns rather than input requirements.

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 verb ('List') and specific resource ('official statistical sources served by this data layer'), with concrete examples (Statistics Canada, FRED). It distinguishes itself from sibling tools by focusing on 'sources' rather than datasets, monitors, or catalog search, and positions itself as the entry point.

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 provides a clear usage context: 'Start here to see what exists.' This implies it is the first tool to use when exploring available data. However, it does not explicitly mention when to use alternatives like list_datasets or search_catalog, so it falls short of fully explicit exclusion guidance.

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