match_logs

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds a specific guarantee that the learned model is not changed, which is useful but not deeply rich. It does not mention matching strategy, output details, or error behavior, though annotations cover the safety profile.

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 concise sentence that leads with the core action and immediately states the key constraint. There is no filler, and every word contributes to the tool's purpose.

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 too sparse for a tool with three parameters and an enum. While annotations and output schema cover safety and return shape, the semantics of extract_parameters and full_search_strategy are missing, making correct invocation uncertain. The tool name and siblings hint at the domain, but the description alone does not fully enable reliable use.

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 description coverage is 0%, so the description must compensate for missing parameter meaning. It only suggests that log_messages holds a batch of log lines, but extract_parameters and full_search_strategy are entirely unexplained. The enum values for full_search_strategy have no semantic guidance, leaving the agent to guess.

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 states a specific verb and resource: 'Match a batch of log lines.' The additional phrase 'without changing the learned model' clearly distinguishes it from training-oriented siblings like train_log/train_logs and from the singular match_log. An agent can understand both what it does and its scope.

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 provides clear context for when to use the tool: when matching multiple log lines and when model mutation is undesired. It does not explicitly name alternatives or exclusions, but the batch wording and the no-model-change constraint strongly imply the distinction from match_log and train_logs.

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