advanced_event_analytics

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 'ML-powered event analytics' and what features can be included, but doesn't disclose critical behavioral traits: whether this is read-only or mutating, permission requirements, rate limits, computational cost, or what happens when defaults are used. For a complex 7-parameter tool with ML components, 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 well-structured with a clear purpose statement upfront, followed by organized parameter and return sections. Every sentence earns its place by providing essential information. It could be slightly more concise by combining some parameter explanations, but the structure makes it easy to parse and understand the tool's functionality.

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 complexity (7 parameters, ML components, no annotations) and the presence of an output schema (implied by 'Returns: Dict'), the description is moderately complete. It thoroughly documents parameters and states the return type, but lacks behavioral context and usage guidance. For such a sophisticated tool, more information about performance characteristics, limitations, or typical use cases would be helpful.

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?

With 0% schema description coverage, the description fully compensates by documenting all 7 parameters in the 'Args' section with clear explanations, defaults, and examples. It adds substantial meaning beyond the bare schema: explaining what 'namespace' is for, providing time period examples, clarifying boolean toggle purposes, and defining analysis_depth options. This is excellent parameter documentation.

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 performs 'Advanced ML-powered event analytics with log/metrics integration and runbook suggestions' - a specific verb ('analyze') with resources ('event analytics') and methods ('ML-powered'). It distinguishes itself from siblings like 'analyze_logs' or 'detect_anomalies' by emphasizing ML, correlation, and runbook generation. However, it doesn't explicitly contrast with 'progressive_event_analysis' or 'adaptive_namespace_investigation' which might be similar.

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 for analysis (e.g., 'analyze_logs', 'detect_anomalies', 'progressive_event_analysis'), there's no indication of what makes this tool unique in terms of use cases, prerequisites, or trade-offs. The agent must infer usage from the name and description alone without explicit direction.

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