Lumino

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

No annotations are provided, so the description carries full burden. It mentions 'NLP for query interpretation, Kubernetes/Tekton entity recognition, and relevance ranking,' which adds some behavioral context. However, it lacks details on permissions, rate limits, error handling, or what 'semantic understanding' entails operationally, leaving significant gaps for a tool with 7 parameters.

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 sections: purpose, behavioral notes, args, and returns. Each sentence adds value, such as explaining the semantic approach and parameter defaults. It could be slightly more concise by integrating the behavioral notes into the purpose statement, but overall it's efficient and front-loaded.

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 7 parameters, 0% schema coverage, no annotations, but with an output schema provided, the description does a good job covering inputs and outputs. The 'Returns' section outlines the response structure, reducing the need for further explanation. However, it lacks context on performance, limitations, or integration with sibling tools, leaving minor 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 0%, so the description must compensate. It provides a detailed 'Args' section explaining each parameter's purpose and defaults, adding meaningful semantics beyond the bare schema. For example, it clarifies 'time_range' options and 'namespaces' auto-detection. This nearly compensates for the lack of schema descriptions, though some nuances like 'severity_levels' values remain unspecified.

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 logs using natural language queries with semantic understanding beyond keyword matching.' It specifies the verb ('search'), resource ('logs'), and distinguishing capability ('semantic understanding beyond keyword matching'), which differentiates it from simple keyword-based search tools among its siblings.

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 no guidance on when to use this tool versus alternatives. With many sibling tools like 'analyze_logs', 'analyze_pod_logs_hybrid', and 'detect_log_anomalies', there is no indication of scenarios where semantic search is preferred over other log analysis methods, nor any prerequisites or exclusions mentioned.

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