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

Annotations already establish readOnly/openWorld/idempotent hints and non-destructive behavior. The description adds valuable context beyond annotations: the JSON-stat2 response format, the data source (px.hagstofa.is), no auth requirement, and the CC BY 4.0 license. It does not mention response size limits or error behavior, but the key operational traits are covered.

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?

Every sentence carries functional weight: the operation, the filter behavior, the required prerequisite, a concrete example, and practical data-source/licensing context. The description is dense but well-organized and front-loads the most important facts before the example.

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?

Given the high-quality schema, output schema, and rich annotations, the description fills all remaining gaps: it names the prerequisite discoverability step, gives a realistic example, states the return format, and notes access and licensing. An agent has everything needed to invoke this tool correctly.

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% and the schema already documents both parameters well. The description adds meaningful semantic reinforcement—explaining that dimension codes are Icelandic words, that 'top' selects N most recent values, and providing a complete example with actual codes and filter types. This helps an agent bridge from abstract schema to real usage.

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 states a specific verb ('Run a statistical query'), a clear resource ('an Iceland statistics table'), and the core capability (dimension filters to slice data). It also distinguishes itself from the metadata tool by explicitly instructing the agent to call iceland-statistics.table_metadata first, making sibling differentiation clear.

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 an explicit prerequisite ('Always call iceland-statistics.table_metadata first') and explains why (codes are often Icelandic words with non-ASCII characters). It provides a concrete example of the expected query structure, making the usage context unmistakable for an agent.

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