glue_job

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 describes what each mode generates, but it does not mention side effects, credentials, filesystem writes, or whether 'manage' implies modifying existing AWS resources. The behavior is not misleading, but it is incomplete for a tool with no annotation safety hints.

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-structured with a one-sentence summary followed by clearly formatted mode sections and parameter lists. It is longer than average, but every section earns its place given the tool's four distinct behaviors, and the front-loaded summary helps an agent orient immediately.

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?

Given the tool's high complexity, 23 parameters, and no annotations, the description does a solid job covering all modes and associating parameters appropriately. The presence of an output schema reduces the need to explain return values. Missing details like allowed values for template_type or source_format and mode-specific examples prevent a perfect score.

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 description coverage is 0%, so the description must compensate. It does by grouping all 23 parameters into their relevant modes and marking required parameters such as pyspark_code and job_name. It adds meaning beyond the bare schema, though it still relies on parameter names to carry most semantic weight rather than describing values or constraints.

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 resource ('AWS Glue job configurations and templates') and the action ('Generate and manage'), and it enumerates four distinct modes that make the tool's scope concrete. It does not explicitly contrast with sibling tools like glue_schema or glue_s3, so it misses the top tier, but the purpose is unambiguous.

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 mode breakdown gives clear context for which mode to choose: template, dynamic_frame, properties, or sql_conversion, each with its own parameter list. However, there is no explicit guidance on when glue_job should be preferred over sibling tools or when a mode should not be used, so usage guidance remains implied rather than explicit.

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