get_logs

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 mentions 'search and retrieve' and 'flexible filtering,' but doesn't disclose critical behavioral traits such as whether this is a read-only operation, potential rate limits, authentication requirements, pagination behavior (beyond the cursor parameter in schema), or what the output looks like. For a tool with 6 parameters and no annotations, 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 appropriately sized and front-loaded: it states the core purpose in the first sentence and adds sibling differentiation in the second. Every sentence earns its place with no wasted words, making it efficient and easy to parse for an AI agent.

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 complexity (6 parameters, nested objects, no output schema, and no annotations), the description is incomplete. It doesn't explain return values, error handling, or behavioral constraints, leaving gaps that could hinder correct tool invocation. For a search tool with multiple parameters and no structured output documentation, more context is needed to be complete.

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 schema description coverage is 100%, meaning all parameters are well-documented in the input schema itself. The description adds minimal value beyond the schema by implying flexible filtering but doesn't provide additional syntax, format details, or usage examples for parameters. With high schema coverage, the baseline is 3, and the description doesn't significantly enhance parameter understanding.

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: 'Search and retrieve logs from Datadog with flexible filtering parameters.' It specifies the verb (search and retrieve), resource (logs from Datadog), and scope (with flexible filtering). It distinguishes from sibling 'get_metrics' by specifying 'for log data.' However, it doesn't explicitly differentiate from other log-related siblings like 'get_logs_field_values,' so it's not a perfect 5.

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 implied usage guidance by mentioning it's 'similar to get_metrics but for log data,' which helps differentiate from one sibling. However, it doesn't explicitly state when to use this tool versus alternatives like 'get_logs_field_values' or other log-related tools, nor does it provide context on prerequisites or exclusions. This is basic differentiation without comprehensive guidance.

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