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list_emr_applications

Retrieve active EMR Serverless applications to monitor job status and access logs for troubleshooting pipeline failures in Airflow environments.

Instructions

List all EMR Serverless applications.

Note: DAGs create temporary EMR apps that are deleted after each run. If an app is not found here, it was already cleaned up — but job run details and S3 logs are still available via get_job_run_details and read_spark_driver_log using the application_id from the Airflow task log.

Args: states: Optional comma-separated state filter (e.g. 'STARTED,CREATED'). env: Target environment — 'dev', 'uat', 'test', or 'prod'. IMPORTANT: Do NOT guess or default. Ask the user which environment if not specified.

Returns a formatted list of applications with IDs, types and states.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
statesNo
envNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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 effectively describes key behaviors: it lists applications, explains that temporary apps are deleted (implying the list may not be comprehensive), and specifies that the output is a formatted list with IDs, types, and states. However, it doesn't mention pagination, rate limits, or authentication requirements, which are common gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and front-loaded with the core purpose, followed by important notes and parameter details. Every sentence adds value: the first states the action, the next explains temporary app behavior, the third guides to alternatives, and the parameter sections provide essential usage context. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (2 parameters, no annotations, but with an output schema), the description is complete. It covers purpose, usage context, parameter semantics, and output format. The output schema handles return values, so the description doesn't need to detail them further. All necessary context for effective use is provided.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 0%, so the description must fully compensate. It does so by clearly explaining both parameters: 'states' as an optional comma-separated state filter with an example, and 'env' as the target environment with specific values and a critical instruction to ask the user if not specified. This adds substantial meaning beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('List all EMR Serverless applications') and distinguishes it from siblings by focusing on EMR applications rather than DAGs, job runs, or other resources. The verb 'list' and resource 'EMR Serverless applications' are precise and unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool vs alternatives: it notes that temporary EMR apps from DAGs are deleted after runs, and if an app is not found here, job run details and logs are available via get_job_run_details and read_spark_driver_log. This clearly delineates the tool's scope and points to complementary tools.

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

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