query_data_lake

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

The description adds significant behavioral context beyond the readOnlyHint annotation. It explains performance requirements (time filter is 'REQUIRED'), provides detailed pagination behavior, describes query syntax (Snowflake SQL), and outlines return structure. While annotations already indicate it's read-only, the description enriches understanding with practical constraints and operational details that aren't captured in structured fields.

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 comprehensive but lengthy and could be more front-loaded. While all content is valuable (macros, best practices, examples, syntax, returns), it's presented as a dense block. The core purpose is clear in the first sentence, but subsequent sections could be better organized or summarized for quicker scanning by an AI agent.

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's complexity (SQL querying with performance constraints, pagination, specific syntax) and the presence of both annotations and output schema, the description is remarkably complete. It covers purpose, usage guidelines, behavioral details, parameter context, examples, and return structure. The output schema means the description doesn't need to explain return values in detail, and it appropriately focuses on operational guidance.

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 80% schema description coverage, the baseline is 3, but the description adds substantial value. It provides extensive context about the 'sql' parameter (time filter requirements, Panther macros, best practices, examples, syntax), explains pagination behavior related to the 'cursor' parameter, and offers performance guidance relevant to 'timeout' and 'max_rows'. This goes well beyond what the schema descriptions provide.

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's purpose: 'Query Panther's security data lake using SQL for log analysis and threat hunting.' This specifies the verb ('query'), resource ('Panther's security data lake'), and context ('log analysis and threat hunting'), distinguishing it from all sibling tools which focus on alerts, detections, users, and other administrative functions rather than direct data lake querying.

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 explicit guidance on when to use this tool: for SQL-based log analysis and threat hunting. It also includes detailed 'Best Practices' and 'Common Examples' sections that guide effective usage, such as starting with summary queries and using time filters. While it doesn't name specific alternatives, the context of sibling tools (which are all about managing alerts, detections, users, etc.) makes it clear this is the primary data querying tool.

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