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

With no annotations provided, the description carries the full burden. It discloses the return structure (summary, time_series, top_devices, breakdown) and includes two concrete examples that illustrate behavior. It does not mention rate limits or auth, but for a read-only analytics tool the return format and examples provide substantial transparency.

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 with clear headers: summary, WHEN TO USE, RETURNS, and EXAMPLES. The opening line is a crisp summary, and every section contributes meaning without unnecessary verbosity. The two examples are illustrative but not redundant.

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 no output schema and four optional parameters, the description is notably complete. It explains the purpose, when to use, what to expect in the response (four categories), and gives two realistic usage examples. It does not explain edge cases like overlapping dates, but such details are not essential for a partner analytics read tool.

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%, so the baseline is 3. The description adds value beyond the schema by showing realistic parameter combinations in examples (e.g., start_date/end_date with group_by 'device' and 'day'). It also clarifies that 'breakdown' corresponds to the group_by dimension, reinforcing the schema's enum.

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 opens with 'Get analytics data for the partner account,' which is a clear verb+resource pairing. The 'WHEN TO USE' section enumerates specific use cases (overall performance metrics, device performance, reports on impressions/earnings, comparing over time) that distinguish this general analytics tool from more specialized siblings like get_network_stats or get_roas.

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 'WHEN TO USE' section provides explicit context: viewing performance metrics, analyzing devices, generating reports, comparing time periods. However, it does not explicitly state when NOT to use this tool or name alternatives, so it stops short of a 5.

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