sigma_get_data_model_spec

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds that the output is a JSON spec containing tables, columns, metrics, and relationships. This is useful behavioral context beyond what the annotations provide, though it does not mention rate limits, auth, or side effects.

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 a single, front-loaded sentence that states the verb, resource, and output contents without redundant words. It is appropriately concise and structured.

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 read operation with one parameter and an existing output schema, the description is largely complete. It clearly conveys the tool's purpose and output. It could be improved by explicitly noting that this tool is for the raw spec vs. the general data model info, but overall it is adequate.

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?

The schema has 0% description coverage for parameters, and the description does not explain data_model_id. However, the single parameter is self-explanatory from its name and the tool context, so the missing description is only a minor gap. The parameter is a straightforward string ID.

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 uses 'Get' with a specific resource 'full code representation (JSON spec) of a data model' and enumerates what is included (tables, columns, metrics, relationships). This clearly distinguishes it from the sibling tool sigma_get_data_model, which likely returns metadata rather than the raw spec.

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 implies usage for retrieving the JSON spec but does not explicitly contrast with sigma_get_data_model or state when not to use it. It provides context about the tool's purpose but omits exclusions or alternative tool recommendations.

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