detect_anomalies_tool

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

Since no annotations are provided, the description fully explains the behavior: it statistically analyzes using the first portion as baseline, flags deviations like rate spikes and error bursts. This is transparent about the method. It does not mention any side effects, but the tool is likely read-only, so no contradiction.

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 brief intro paragraph and a bullet-like Args list. Every sentence adds value; no fluff. It is concise while being informative, fitting within a few lines.

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 complexity (6 parameters, no annotations), the description covers all necessary context: purpose, usage step (after load_run), algorithm (baseline approach), parameter details, and types of anomalies. The presence of an output schema means return values need not be described, so completeness is excellent.

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?

Despite 0% schema coverage, the description provides thorough explanations for all 6 parameters in the Args section, including defaults, constraints (e.g., 0.0-1.0 for min_severity_score), and purpose. This adds essential meaning beyond the schema's type and default.

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: 'Detect anomalous patterns in a ROS2 log run.' It specifies the verb (detect), resource (anomalies in log run), and distinguishes from siblings like compare_runs or query_logs by focusing on anomaly detection using a baseline.

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 gives a clear usage guideline: 'Use after load_run.' It implicitly tells when to use (post-load) and lists specific anomaly types, which helps differentiate. However, it does not explicitly state when not to use or provide alternatives, which prevents a 5.

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