Detecter les anomalies d'execution

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

No annotations provided, so description carries the burden. It discloses types of anomalies (error rate, latency, cost, retry loops) and severity classification, but does not mention if the tool is read-only, auth needs, rate limits, or side effects. Adequate but not comprehensive.

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?

Description is a single sentence with a bullet list, front-loading the purpose and types of anomalies. No redundant information. Efficient for its length.

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?

With 6 parameters and no output schema, the description covers the tool's purpose and anomaly types but lacks details on output structure or how to interpret results. The response_format parameter hints at formats but not explained.

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% (all parameters have descriptions). The tool description adds context about anomaly detection logic but does not provide additional parameter-level semantics beyond the schema. Baseline 3 is appropriate.

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?

Description clearly states the tool analyzes events to detect multiple types of anomalies (error rate, latency, cost, retry loops) and classifies them by severity. It distinguishes from siblings like obs_event_log (basic logging) and obs_metrics_summary (metrics) by specifying unique detection logic.

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 explains what anomalies are detected but does not explicitly state when to use this tool instead of siblings (e.g., obs_events_search for specific events, obs_metrics_summary for summary stats). No when-not-to-use guidance is provided.

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