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

Annotations already mark this read-only and idempotent, and the description adds valuable behavioral details beyond them: the SDMX key position ordering, the 200-code cap per dimension, the total_codes count, the backing Bundesbank SDMX 2.1 REST API, and that no auth is required.

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 compact and front-loaded, covering purpose, source of the parameter, output behavior, downstream use, data endpoint, and auth in just a few sentences. Every sentence earns its place.

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 for a single-parameter reference tool with an output schema and read-only annotations. It explains where the input comes from, what the output contains, the cap behavior, and how the result should be used to build a data key.

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's property description already explains the parameter, its source, examples, and the output it produces. The tool description reinforces this context but does not add significant new parameter-level meaning beyond the schema.

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 and resource: get dimensions and valid codes for a Bundesbank dataflow. It clearly distinguishes itself by explaining the dataflow_id comes from bundesbank-timeseries.dataflows and that the output is needed for bundesbank-timeseries.data, separating it from sibling tools.

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 clear context on where this tool fits in the workflow: after selecting a dataflow_id and before building the key for bundesbank-timeseries.data. It does not explicitly say 'when not to use' alternatives, but the pipeline role is unambiguous.

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