natural_language_query

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

With no annotations provided, the description carries full burden. It states the tool returns a plan but does not disclose side effects, read-only nature, permissions, or error behavior. The description lacks safety and operational context.

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 two sentences plus a clarifying paragraph, front-loaded with the primary action. No extraneous content, efficient.

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 covers purpose and output plan but omits details like plan format, error handling, or constraints on query complexity. Since an output schema is present, the agent can infer structure, so completeness is adequate but not comprehensive.

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 coverage is 100% with clear parameter descriptions for query and dataset_name. The tool description adds context that the query is interpreted against the dataset's schema, but this is marginal improvement over the schema.

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 interprets a natural language query against a dataset and returns a structured plan for execution. This differentiates it from sibling tools like analyze_data or execute_code by focusing on plan generation rather than direct analysis.

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 implies usage when an NL query needs to be converted to steps, but it does not explicitly state when to use this tool vs alternatives like analyze_data or compare_datasets. No when-not or alternative recommendations are provided.

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