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jobs_create

Create a Databricks job with a name and tasks; returns the job ID.

Instructions

Create a Databricks job. Returns job_id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesJob name
tasksYesList of task dicts. Each task has a ``task_key`` and exactly one of: ``notebook_task``, ``spark_jar_task``, ``python_wheel_task``, ``spark_python_task``, ``spark_submit_task``, ``sql_task``, ``dbt_task``, ``pipeline_task``, ``run_job_task``, ``condition_task``, ``for_each_task``, ``clean_room_notebook_task``, ``clean_rooms_task``, ``trigger_dbt_task``.
tagsNo
max_concurrent_runsNo
scheduleNoSchedule cron. Fields: quartz_cron_expression, timezone_id, pause_status
queueNoQueue settings (enabled, name)
timeout_secondsNo
email_notificationsNo
webhook_notificationsNoOn-start/on-failure/on-success webhook URLs
triggerNoOptional trigger (file_arrival | periodic | continuous | table_update | on-demand | dbt-cloud)
job_clustersNo
parametersNo
descriptionNo
continuousNo
git_sourceNo
idempotency_tokenNo
access_control_listNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

Annotations already indicate readOnlyHint=false (mutation). The description adds that it returns job_id, which is useful but does not elaborate on other behaviors like required permissions, idempotency, or potential side effects.

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

Conciseness3/5

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

The description is a single sentence, making it concise but lacking structure. It states purpose and return value, but omits important details that could be organized into a brief, scannable format.

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

Completeness1/5

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

Given the tool's complexity (17 parameters, 2 required) and low schema coverage, the description is severely incomplete. It fails to explain core concepts like task definitions, job scheduling, or cluster configuration, leaving the agent with insufficient context for correct usage.

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

Parameters1/5

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

The description provides no parameter-specific information. With only 35% schema description coverage, the description does not compensate by explaining key parameters like tasks, schedule, or tags beyond what the schema already includes.

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 verb 'Create' and the resource 'Databricks job', and distinctly differentiates from sibling tools like jobs_update, jobs_reset, and jobs_delete which are for updating, resetting, or deleting jobs.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives such as jobs_update or jobs_reset. No prerequisites, context, or exclusions are mentioned.

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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