datamodel.get_model_schema

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

Annotations already establish readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context beyond the annotations: it explains the row-per-column emission, type-code mapping, success/error return shape, and the returned field names. This gives the agent a clear picture of what to expect when calling the 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 well-structured and front-loaded with the core purpose, followed by output behavior and return fields. It is mostly efficient, though 'one row per column' appears twice (once in the first paragraph and again in the return description), which is a minor redundancy.

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, read-only schema retrieval tool with no output schema, the description is complete: it covers the input, the resolution behavior, the output format, the returned fields, and the error return shape. An agent has enough information to invoke it correctly and interpret the result.

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%, with datamodel_name documented as 'Name (title) of the data model to retrieve the schema for.' The description reinforces this by saying the model is 'resolved by name,' but adds no substantially new semantic detail beyond what the schema already provides. Baseline 3 is appropriate given the high schema coverage.

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 clearly states a specific action ('Retrieve the schema of a data model') and resource, and goes further by describing the output granularity ('one row per column') and the mapping of numeric type codes. However, it does not explicitly distinguish itself from the closely related sibling datamodel_get_table_schema, so it stops short of full differentiation.

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 usage context is implied: use this when you need the full column-level schema of a named data model. The description does not explicitly state when not to use it or point to alternatives such as datamodel_get_table_schema or datamodel_get_all_datamodel, leaving the agent to infer the proper selection.

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