export_to_data_warehouse

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly states that the export is 'simulated,' indicating no actual write occurs, and that data goes 'directly' to the warehouse, implying a single-hop transfer. It also reveals the source and destination types. While it doesn't mention permissions or rate limits, the simulated nature implies minimal side effects, providing meaningful transparency beyond the schema.

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, dense sentence that front-loads the action verb and packs in all essential context: the simulated nature, data transformations, source, and destination. There is no redundant or filler content, and the structure is efficient for an AI agent to parse.

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 tool has an output schema, so return values are covered. The description provides a solid high-level overview and context (pipeline stage, destination). However, parameter semantics are incomplete, especially for 'max_records,' and while the warehouse options listed help with 'target_warehouse,' the connection is not explicit. Overall completeness is adequate but leaves important gaps.

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 description gives zero explanation of the parameters. It does not mention 'target_warehouse' or 'max_records,' nor does it clarify their purpose. Schema coverage is 0%, so the description offers no help beyond the raw property definitions. Even the optional max_records, which controls record count, is entirely ignored, making it impossible for an agent to understand parameter semantics from the description.

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 function: simulating an export from the Cold Storage DB to an enterprise data warehouse. It specifies the action (exporting), the source (Cold Storage DB), the data characteristics (anonymized, prioritized, stitched), and the destination (Snowflake/ClickHouse/BigQuery). This distinguishes it from sibling ingest/fetch tools by indicating a data egress operation.

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?

Usage context is implied through the description's references to 'anonymized, prioritized, and stitched' data, suggesting it should be used after pipeline processing steps. However, there is no explicit guidance on when to choose this tool over alternatives, nor exclusions or prerequisites. The lack of explicit when-to-use guidance keeps it at a mid-level score.

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