group_semantic_patterns

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

No annotations are provided, so the description carries the full burden of disclosing behavioral traits. It reveals the algorithm and the type of output (core events and parameter distributions), but does not state whether the tool is read-only, has side effects, or requires any preconditions. This is partially transparent but leaves significant behavioral aspects undisclosed.

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 a single, concise sentence that front-loads the core purpose and algorithm. No unnecessary words.

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?

The tool has no output schema, so the description should ideally explain return values; it hints at 'core events and parameter distributions' but does not specify the structure. It also does not contextualize usage among the sibling tools. Given the moderate complexity (4 parameters), the description is adequate but not fully complete.

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?

All parameters have descriptions in the input schema, providing full coverage. The tool description adds the algorithm name and the concept of parameter distributions, but does not elaborate on the individual parameters. Therefore, it meets the baseline of 3 without additional value.

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 clusters similar log messages using the Drain algorithm, specifying the resource (log messages) and the outcome (isolate core events and parameter distributions). This distinguishes it from sibling tools like query_log_pattern or detect_error_anomalies.

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 a use case for grouping similar log messages but does not explicitly state when to prefer this tool over alternatives like query_log_pattern or discover_log_schema. There is no mention of exclusions or prerequisites, so the guidance is only implicit.

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