get_logs_field_values

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves 'possible values' but doesn't clarify whether this is a read-only operation, if it requires specific permissions, how it handles large datasets, or what the output looks like (e.g., format details beyond the 'format' parameter). For a tool with 5 parameters and no annotation coverage, this is a significant gap in 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 a single, efficient sentence that front-loads the core purpose without unnecessary words. It directly states what the tool does and its utility, making it easy for an agent to parse quickly. Every part of the sentence earns its place by conveying essential information.

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?

Given the tool's moderate complexity (5 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the basic purpose but lacks details on behavioral traits, usage context, and output expectations. Without annotations or an output schema, the agent must infer too much, making this minimally viable but with clear gaps.

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%, meaning all parameters are well-documented in the input schema itself (e.g., 'field_name' includes examples, 'time_range' has an enum). The description adds minimal value beyond this, as it doesn't explain parameter interactions or provide additional context like why certain fields are listed. Baseline 3 is appropriate when the schema does the heavy lifting.

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's purpose: 'Get possible values for a specific log field to understand filtering options'. It specifies the verb ('Get'), resource ('possible values for a specific log field'), and context ('to understand filtering options'). However, it doesn't explicitly differentiate from sibling tools like 'get_logs' or 'get_metric_field_values', which prevents a perfect score.

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 provides minimal guidance, mentioning only that it helps 'understand filtering options'. It doesn't specify when to use this tool versus alternatives like 'get_logs' (which might retrieve actual logs) or 'get_metric_field_values' (for metrics), nor does it mention prerequisites or exclusions. This lack of explicit context limits its utility for an agent.

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