datamodel.get_all_datamodel

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

Beyond the readOnlyHint and non-destructive annotations, the description discloses the internal API route, the fact that it sends a POST request, the Linux-only platform constraint, and the exact success/error return shapes. This adds meaningful behavioral context that annotations alone do not provide.

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 front-loaded with the core purpose, then adds the API detail, return type, and platform caveat in a logical order. Every sentence provides useful information for selection and invocation, with no fluff or repetition.

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-parameter read-only metadata tool, the description is complete: it explains the API mechanism, the returned metadata fields, error behavior, and when to use an alternative. There is no output schema, so the explicit return format in the description fully covers what an agent needs to interpret results.

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 input schema has no parameters, so the description needs no parameter-level explanation. With zero parameters, baseline is 4, and the description appropriately focuses on output and platform behavior instead.

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 the tool retrieves metadata for all data models via an internal API, with a specific endpoint and GraphQL query. It also distinguishes itself from the related sibling get_elasticubes by noting the Linux-only route, so an agent can tell them apart.

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 explicitly explains when to use this tool versus the alternative: on Windows-based Sisense, use get_elasticubes instead. It also clarifies that this tool accesses internal metadata not available through standard public endpoints, giving clear selection guidance.

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