Log Anomaly Detection

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

Annotations already declare readOnlyHint and idempotentHint, so safety is clear. The description adds valuable context by noting the LOGJUICER_URL configuration requirement and the parameter alternatives (url vs uuid+tenant), going beyond the annotations without contradicting them.

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 concise and well-structured: a one-sentence purpose, a requirement line, and a clean Args list with no redundant text. Every sentence earns its place.

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 an output schema present, the description needn't explain return values. It covers the essential context: purpose, environment requirement, and parameter semantics. It lacks some usage scenarios, but for a simple tool, the essentials are covered.

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 coverage is 0%, so the description must compensate, and it does excellently. It explains each parameter (uuid as build ID, tenant with env default, url as alternative to uuid+tenant), providing meaning and relationships not present in the schema.

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 detects anomalous log lines using LogJuicer ML-based analysis. This is specific and distinguishes it from sibling tools like get_build_log or get_build_failures, which focus on raw logs or failures rather than ML-driven anomaly detection.

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 implies usage for anomaly detection but does not explicitly state when to use this tool over alternatives or provide exclusions. It mentions a prerequisite (LOGJUICER_URL) but lacks clear guidance on selection among sibling tools.

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