Lumino

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

With no annotations provided, the description carries full burden but offers limited behavioral insight. It mentions the detection methods (error frequency, pattern repetition, timestamp analysis) and return structure, but lacks details on permissions, rate limits, error handling, or what constitutes an anomaly. This is inadequate for a tool with potential complexity.

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 and front-loaded: a clear purpose statement followed by 'Args:' and 'Returns:' sections. Every sentence adds value without redundancy, making it efficient and easy to parse.

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 no annotations, 0% schema coverage, but an output schema exists, the description is moderately complete. It covers purpose and parameters well, but lacks behavioral context (e.g., how anomalies are defined, performance implications). The output schema handles return values, so that gap is mitigated, but overall it's adequate with room for improvement.

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 0%, so the description must compensate. It adds meaningful semantics: 'logs' as 'Raw log content (newline-separated entries)', 'baseline_patterns' as 'Optional expected error patterns for comparison', and 'severity_threshold' with values and sensitivity levels. This clarifies parameter purposes beyond the schema's basic types.

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 function: 'Detect anomalies in log data using error frequency, pattern repetition, and timestamp analysis.' It specifies the verb ('detect'), resource ('anomalies in log data'), and methods, but doesn't explicitly differentiate from sibling tools like 'detect_anomalies' or 'analyze_logs', which appear related.

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?

No guidance is provided on when to use this tool versus alternatives. The description lists parameters and returns but doesn't mention sibling tools or contexts where this tool is preferred, such as for anomaly detection versus general log analysis.

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