Append Rows to Table

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

No annotations are provided, so the description must fully disclose behavior. It only mentions the engine and row format. It does not describe whether the operation is idempotent, if it handles partitioning, error handling, or performance characteristics. For a mutation tool, this omission is significant.

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?

Two sentences with no fluff. First sentence states purpose, second clarifies the rows parameter. Could be improved by structuring info into short paragraphs or bullet points for readability, but currently 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 tool has 9 parameters (6 required) and no output schema. Description lacks practical context like success/failure behavior, typical use cases, or how to verify the append. For a complex tool with many parameters, this is insufficient for an agent to use confidently.

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%, so parameters are already well-described structurally. The description adds that rows must be a list of dicts, each representing a row, which is already implied by the schema type (array of objects). Minimal added value over schema descriptions.

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 action (Append rows) and the resource (Iceberg table using PyIceberg engine). It distinguishes from sibling tools like create_table or import_csv_to_table by specifying direct row appending.

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 does not explicitly state when to use this tool vs alternatives like import_csv_to_table or import_parquet_to_table. It implies usage when data is already in a list of dictionaries, but lacks explicit guidance on prerequisites or when not to use.

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