observe_query

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

With no annotations provided, the description bears full responsibility for disclosing behavior. It only states that the tool returns up to rowCount rows, but does not mention if the query is read-only, authentication requirements, rate limits, or whether the query can mutate data. The description is insufficient for understanding behavioral implications.

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 sentence that efficiently conveys the core purpose. It is front-loaded and contains no superfluous words. Every part earns its place.

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 has 6 parameters and no output schema, the description is too minimal. It does not explain the return format (e.g., table, JSON), error handling, or time range behavior. The description leaves important gaps for an agent to correctly use the tool.

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?

The input schema has 100% coverage with descriptions for all 6 parameters. The description adds no additional meaning beyond the schema; it simply echoes the row_count parameter. According to guidelines, with high schema coverage, baseline is 3, and the description does not exceed that.

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 runs a raw OPAL pipeline query against Observe and returns up to rowCount rows. This distinguishes it from sibling tools like search_entity_logs (which are more specific) and list_datasets (which lists datasets). The verb 'run' and resource 'raw OPAL pipeline query' are specific and unambiguous.

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?

No guidance on when to use this tool versus alternative tools like search_entity_logs or search_service_logs. The description does not mention when to choose this over other query-related tools or any prerequisites.

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