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

The description adds rich behavioral context beyond the annotations: explains the key filter semantics with wildcards, the consequence of omitting key (fetch all series, potentially large), and the return format ('decoded series with their dimension labels and per-period values'). This goes far beyond the read-only/idempotent flags.

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, well-organized, and front-loaded with the core purpose. It includes a helpful inline example while keeping every sentence informative. No filler or redundant phrasing.

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?

Despite having six parameters and no output schema, the description covers the essential operational details: prerequisite tool, key syntax, wildcard logic, the risk of fetching all data, and the return shape. It gives the agent enough to use the tool safely and 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%, so baseline is 3. The description significantly enhances the most complex parameter (key) by explaining the dot-separated positions and wildcard behavior, and clarifies that omitting key vs passing an empty string are equivalent. Other parameters are left to the schema, which already provides adequate descriptions.

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 opens with the specific action and resource: 'Pull observations from a STATEC dataset.' It clearly distinguishes this from sibling tools like dataflow_structure (which provides structure, not observations) and list_dataflows. The detailed key syntax and example further reinforce the tool's unique purpose.

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?

It gives explicit directive to 'Fetch dataflow_structure first to know the dimension order and valid codes,' which is a prerequisite and alternative-tool guidance. It also warns about the risk of fetching all series ('caution, this can be large'), telling the agent when to be careful. The example shows a concrete invocation.

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