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

With no annotations, the description carries the full burden of behavioral disclosure. It explains what the returned schema contains, including cross-dataset link_keys and semantics constraints, and even warns that the district code is 'NOT a county FIPS.' This is valuable context beyond the tool name.

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 dense but well-packed into a single informative block. It front-loads the core purpose ('Schema for one dimension') and then adds necessary detail, examples, and a critical caveat. It is not overly long given the complexity of the domain.

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 covers the key concepts needed to understand what will be returned and when to call it, and an output schema exists to fill in structural return details. The warning about the 5-digit code versus county FIPS and the semantics explanation make this complete for an agent preparing a link_query join.

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 schema provides only a 'name' parameter with 0% description coverage, so the description must compensate. It implies that 'name' identifies a dimension such as districts, schools, counties, or demographics, but it never explicitly states how to format or supply the value. Some meaning is added, but the parameter guidance remains indirect.

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 that the tool returns the schema for a dimension, enumerating the primary key, attribute columns, link_keys, and semantics. It is specific about the resource type, but it does not explicitly differentiate itself from siblings like describe_dataset or get_dimension, relying on context rather than naming them.

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 gives clear usage context: 'Read this before writing a link_query join.' This tells the agent when to invoke it, though it does not explicitly state when not to use it or name alternatives such as describe_dataset.

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