etl_star_cdc

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

With no annotations, the description must carry the disclosure burden. It does disclose the append behavior and source/target, but omits side effects, asynchronous job behavior, idempotency, or failure handling.

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 front-loaded sentence with no filler, efficiently conveying source, target, and append mode. It is concise, though this conciseness comes at the cost of behavioral and parameter detail.

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?

For an ETL write operation with no annotations or output schema, the description is too sparse. It does not explain the meaning of as_job, the relationship to etl_star_historical, or what happens on failures, empty change feeds, or duplicate data.

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?

There is one parameter, as_job, with 0% schema description coverage. The description does not mention or explain this parameter, leaving the agent with only the parameter name and default value as hints.

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 names a specific verb ('Apply'), the exact data source ('Delta change feed from OLTP customer_order'), target ('fact_sales'), and mode ('append'). This clearly distinguishes it from siblings like etl_star_historical and generate_historical_oltp.

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 this is for incremental CDC appends, but it does not explicitly name alternatives, state prerequisites, or describe when not to use it. Usage context is inferable but not spelled out.

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