train_log

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

Annotations already mark the tool as non-read-only and non-idempotent, and 'Train Drain3' confirms a state-mutating operation. The description adds that training is per single line and that the call returns the cluster/parameters, but it does not disclose whether this mutates persistent state or requires prior initialization. No contradiction with annotations.

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?

A single sentence with the verb and target first, no filler or repetition. Every phrase ('one log line', 'cluster', 'extracted parameters') carries information about input or output.

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?

For a simple two-parameter tool with an output schema, the core call is adequately described. However, the lack of explicit sibling routing and the absence of any note on repeated training behavior or model state leave the description slightly incomplete in context. The output schema relieves it from documenting return structure.

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?

Context signals show 0% schema description coverage, so the description must add meaning beyond bare names. It does map log_message to 'one log line' and connects 'extract_parameters' to the return of extracted parameters, but it does not explain the boolean's effect when false or its default. This is only partial compensation for the missing schema descriptions.

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 names a specific action ('Train'), the target resource ('Drain3'), the input granularity ('one log line'), and the expected output ('cluster and extracted parameters'). The singular 'one log line' also implicitly separates it from the sibling 'train_logs'. This is specific enough for an agent to select it.

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?

It conveys a clear context: use this tool to train on exactly one log line and retrieve the resulting cluster and parameters. It does not, however, explicitly state when to prefer the plural sibling train_logs or exclude batch training, so the agent must infer the boundary from naming.

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