get_measure_codes

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

No annotations are provided, so the description carries the full transparency burden. It does explain the return shape ('Count and list of measure codes') and defines what a measure is, but it does not disclose error handling, defaults, or explicitly state that this is a read-only operation beyond the inferred 'fetch' semantics.

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 appropriately sized and well-structured: a one-sentence summary, a clarifying definition, and docstring-style Args/Returns sections. Every sentence adds value without redundancy or unnecessary detail.

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 simple two-parameter lookup with an output schema, the description covers the core return shape and domain concept. It lacks an example or explicit relationship to sibling tools like get_dimensions, but it is sufficient for an agent to understand the basic operation.

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 0%, but the description does include an Args section with 'catalog: Catalog identifier' and 'dataset: Dataset identifier.' These are minimal and do not mention optionality or defaults (e.g., catalog defaults to 'CBS'), so they add only marginal 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 opens with 'Fetch measure code definitions for a dataset,' which is a clear verb+resource statement. It further distinguishes this tool from siblings like get_dimensions and get_observations by defining measures as 'columns containing actual measurement values in observations.'

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?

There is no explicit guidance about when to use this tool versus alternatives such as get_dimensions, get_observations, or query_observations. The description only gives a generic 'fetch' purpose and does not mention exclusions or prerequisites, leaving the agent to infer usage from the tool name and sibling list.

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