train_logs

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

Annotations already mark the operation as non-read-only and non-idempotent, so the description's lack of a mutation warning is acceptable. It adds value by noting order sensitivity, but it does not disclose whether training appends to or replaces existing model state, or whether a separate save_state call is required to persist the result.

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?

One short sentence with no filler. The core action, resource, input type, and order requirement are all front-loaded, and every word contributes.

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 description is adequate for a simple batch-training call with an output schema and informative annotations, but it leaves out important operational context: what extract_parameters does, whether training is additive or replaces prior state, and whether persistence requires save_state. These are gaps an agent would benefit from knowing.

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?

With 0% schema description coverage, the description must compensate. It adds meaning for log_messages by calling them 'a batch of log lines in the provided order,' but it never explains the extract_parameters boolean, leaving the optional parameter semantically uncovered.

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 names a specific verb ('Train'), a concrete resource ('Drain3'), and an explicit input mode ('batch of log lines in the provided order'). This clearly sets it apart from the singular sibling train_log and the inference siblings like match_log.

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 phrase 'batch of log lines' and 'provided order' give a clear context: this tool is for multi-line training where order matters, not for single-line training or matching. It does not explicitly name alternative tools or list exclusions, but the context is strong enough for an agent to select it.

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