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

With no annotations, the description carries the full burden, and it does describe the shape of what is returned (per-field metadata, semantics, reliability). It is a static, zero-param reference lookup so risk context is minimal, but it doesn't state whether it covers all fields at once or how the output is organized.

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?

Two tight sentences: the first defines content, the second defines when to call. The trigger is front-loaded after the content summary with zero filler, and every clause 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?

For a zero-param reference tool with no output schema, the description supplies enough to call it correctly and know what comes back. Minor gap: it doesn't clarify that the entire field catalog is returned rather than a subset.

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?

Zero parameters, so the schema has nothing to document and the baseline is 4. The description correctly signals a parameterless, whole-reference lookup, though it doesn't explicitly note that no field argument is needed.

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?

States a specific verb (get) and resource (field reference) and enumerates exactly what the documentation covers: type, unit, nullability, observed vs. model estimate, how to read it, and which tool filters/reports it. It is clearly distinguishable from siblings like get_field_coverage or get_field_distribution, which return data rather than documentation.

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?

Provides an explicit trigger: call this when the user asks what a field means or how reliable it is. That is clear context for invocation, but it names no alternatives or exclusions (e.g., get_source_reference or get_data_quality_reference), leaving the boundary between sibling reference tools to inference.

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