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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds non-obvious behavioral context beyond annotations: it 'reads only the active API key's organization' and 'is not separately rate-limited.' These are meaningful operational details that help an agent decide to call 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?

Three dense but focused sentences: purpose, usage positioning, and return values. Every sentence adds value and the most important routing guidance appears early. No filler or 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 tool with rich annotations and no output schema, the description is complete: it covers when to use it, what it does, its behavioral traits, and the exact fields returned. An agent has everything needed to select and invoke it correctly.

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%, so the schema already fully documents dataset_id. The description adds little beyond what the schema provides: it mentions 'saved dataset_id' and the list_data workflow, but this is context already implied by the schema's 'from connect_data or list_data.' Baseline 3 is appropriate.

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 and resource: 'Get the low-token dataset selection summary for a saved dataset_id.' It distinguishes itself by emphasizing 'low-token' and 'selection summary', which sets it apart from heavier tools like get_data_schema or get_dataset_status. This is a clearly differentiated purpose.

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 explicit usage context: 'Use this after list_data(search=..., compact=true) before paying for the full schema payload.' This tells the agent exactly when to invoke it and what to avoid, implicitly naming the alternative (full schema retrieval) without ambiguity.

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