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

With no annotations provided, the description carries the full burden. It explains what the tool produces (flat delimited string), the list of available meta-fields, and includes a concrete example. It does not discuss error behavior, permissions, or edge cases, but for a read/export operation this is reasonable.

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 concise and well-structured: it opens with the core purpose, then provides usage context, then lists available meta-fields, and ends with a representative example. Every sentence earns its place with no 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 tool with 3 parameters and no output schema, the description is quite complete. It explains the output format, the selectable fields, and demonstrates invocation. Minor gaps remain about row separation or exact output format details, but the example clarifies most usage.

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 coverage is 100% and the schema already describes each parameter. The description adds value by listing all valid meta-field values and providing a worked example, which reinforces the semantics of 'fields' and 'separator' beyond the schema's default list.

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 exports attributes of a data model as a flat delimited string with caller-selected meta-fields. This specific verb+resource+output format distinguishes it from siblings like get_attributes_for_model or get_attribute_details.

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 says 'Use this when a data engineer needs a CSV-ready or pipe-delimited listing' and gives concrete use cases (database column mapping, ontology alignment, spreadsheet import). It does not explicitly mention alternatives or when not to use it, but the context is clear enough.

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