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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety. The description adds behavioral context by clarifying that the tool returns metadata only, not actual dataset records, and enumerates the specific metadata components. This helps set expectations beyond what the annotations convey.

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 concise and well-structured: one sentence explaining the main purpose with an enumerated list, followed by a short usage directive. Every word earns its place, with no redundant or filler content.

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?

The description is complete given the tool's simplicity and rich annotations. It explains what the tool returns, when to use it (after search_datasets), and the parameter is thoroughly documented in the schema. No output schema exists, but the description fully lists the return content.

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?

Schema coverage is 100% with a highly descriptive dataset_id parameter including format (portal:id), examples, and how to obtain it from search_datasets. The tool description itself adds no parameter information beyond what the schema already provides, so the schema carries the semantic load.

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's function: 'Get complete metadata for a dataset' and enumerates specific types of metadata (fields, types, descriptions, API endpoint, license, formats, last update). This verb+resource phrasing distinguishes it from sibling tools, especially list_dataset_fields (which likely only returns field information) and query_dataset (which returns data, not metadata).

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 explicit usage context: 'Call after search_datasets to get full details of a specific dataset.' This tells the agent when to use the tool. It does not explicitly mention exclusions (e.g., 'use only after search, not for querying data'), but the context and sibling tool names make the intended use clear. The guidance is clear and actionable.

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