glue_data

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

No annotations are provided, so the description carries full burden. It uses the ambiguous verb 'generate' without clarifying whether it actually creates AWS resources, requires permissions, or has side effects. There is no mention of what happens upon invocation or the nature of the generated artifact.

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 well-organized with a clear opening line stating the tool's purpose, followed by mode-specific bullet points listing parameters. It efficiently packs a large amount of information without redundancy or fluff, though it could be slightly shorter.

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 the three modes and their parameters adequately. However, it lacks explanation of what the tool returns (output schema exists but not described) and behavioral implications like whether the jobs are actually deployed or just configuration generated. Some operational context is missing given the tool's complexity (14 params, 3 modes).

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 input schema has 0% description coverage, but the description groups parameters by mode and indicates which are required for each mode (e.g., 'source_database (required)'), adding crucial context beyond the schema structure. Default values like 'cdc_column=last_modified' are also mentioned, helping the agent select correct parameters.

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 first sentence clearly states 'Generate AWS Glue data processing jobs — incremental, CDC, bookmarks', specifying the verb (generate) and resource (Glue data processing jobs) and listing three distinct modes. This distinguishes it from sibling tools like glue_job and glue_schema which have different purposes.

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 provide explicit guidance on when to use this tool versus sibling tools (e.g., glue_job) nor when to choose between the three modes. It only lists parameters per mode without comparative context or usage scenarios. The agent must infer usage from mode names alone.

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