Statistics Finland — List Statistical Tables

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

Annotations already indicate readOnly, idempotent, and non-destructive behavior. The description adds useful disclosure beyond that: no API key required, CC BY 4.0 licensing, official government open data, and the exact return fields. There is no contradiction with annotations. It does not cover pagination or invalid-category behavior, but those are minor for this read-only listing tool.

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 front-loaded with the core purpose and use case, then adds return fields, category examples, an API URL, and licensing. The category examples are somewhat repetitive with the schema, but every sentence adds useful context. It is informative without being bloated.

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 single-parameter, low-complexity tool with an output schema, the description is largely complete: it explains what the tool does, when to use it, what it returns, how to continue with the returned IDs, and authentication/licensing. It does not mention edge cases like unknown categories or pagination, but these are not critical given the open-world hint and simple discovery purpose.

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 description coverage is 100%, and the schema already explains the 'category' parameter with examples. The description repeats similar category examples and adds 'Returns all tables available in that statistical category,' but this is marginal beyond the schema. Baseline 3 is appropriate because the schema carries the parameter documentation burden.

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 and resource: 'List all available data tables in a Statistics Finland (StatFin) category.' It clearly states what is returned (table IDs, titles, last-updated timestamps), making its discovery/catalog role distinct from sibling data-retrieval tools like statfin.economy.consumer_price_index. The purpose is unambiguous and differentiated by function.

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 usage context: 'Use this to discover what datasets are published in a given statistical domain.' It also tells the agent that returned table IDs can be queried via the PxWeb API. However, it does not explicitly state when NOT to use this tool or name alternative sibling tools for fetching actual data, so it falls short of a 5.

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