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Manage job runs

manage_job_runs
Destructive

Submit, monitor, inspect, cancel, or repair Databricks job runs; retrieve task outputs and errors, wait for completion, and manage run records.

Instructions

Start, monitor, inspect, cancel and repair Databricks job runs.

  • submit: spec = one-time run (run_name, tasks [task_key + task type + compute], environments, git_source, timeout_seconds, idempotency_token, ...). Returns run_id with status 'pending'.

  • list (job_id, active_only/completed_only, start_time_from/to), get (run_id: state, per-task states, error messages), wait (run_id, timeout_seconds: bounded poll), get_output (run_id[, task_key]: notebook exit values, logs, errors/stack traces; multi-task runs are expanded per task).

  • cancel (run_id), cancel_all (job_id or all_queued_runs), delete_run (run_id): DESTRUCTIVE, need confirm.

  • repair (run_id, spec: rerun_all_failed_tasks | rerun_tasks, rerun_dependent_tasks, latest_repair_id, job_parameters, ...): EXECUTION.

Safety classification: depends on input (DESTRUCTIVE, EXECUTION, READ_ONLY, SECURITY_SENSITIVE).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
specNoRequest body fields for create/update, using the Databricks REST API field names (snake_case). Unknown fields are rejected.
waitNosubmit/repair: poll until the run finishes (bounded).
actionYessubmit: one-time run (spec = runs/submit body); list: runs (filters); get: run with task states and errors; get_output: outputs/errors per task; wait: poll until finished (bounded); cancel: one run; cancel_all: all active runs of a job; repair: re-run failed/selected tasks; delete_run: delete a finished run record.
job_idNolist/cancel_all: restrict to this job.
run_idNoRun id (get/get_output/wait/cancel/repair/delete_run).
confirmNoSet to true ONLY after the user has reviewed the plan returned by a previous call with status 'confirmation_required'. Required for destructive/security-sensitive actions.
dry_runNoIf true, validate and return the planned change without executing it.
task_keyNoget_output: only this task's output.
page_sizeNoMax items to return (server caps this).
page_tokenNonext_page_token from a previous response.
active_onlyNolist: only active runs.
start_time_toNolist: runs started at/before (epoch ms).
completed_onlyNolist: only completed runs.
all_queued_runsNocancel_all: cancel queued runs (of all jobs when job_id is omitted).
start_time_fromNolist: runs started at/after (epoch ms).
timeout_secondsNoMax seconds to wait (capped by server).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNo
pageNo
planNo
toolYes
actionNo
safetyNo
statusNosuccess
summaryYes
warningsNo
next_stepsNoSuggested follow-up calls.
request_idNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare destructiveHint=true, readOnlyHint=false, and openWorldHint=true, so the safety profile is partly covered. The description does add value by pinning destructiveness to specific actions and describing the confirm-after-confirmation_required workflow plus the bounded-poll behavior of wait. However, it does not describe permissions requirements, rate limits, or what state is left behind after a destructive action, so it remains moderate rather than rich.

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

Conciseness4/5

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

The summary sentence is front-loaded, followed by a compact bulleted map of action-to-parameter semantics and a one-line safety note. For a 9-action, 16-parameter tool the length is justified, though the per-action bullets partly restate the schema's own action enum descriptions.

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

Completeness4/5

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

With an output schema present, return values need not be re-explained, and the description covers action semantics, the confirmation gate, and per-action destructive classification. It is complete enough to call correctly, with only minor gaps such as pagination expectations that the schema already handles.

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

Parameters3/5

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

Schema description coverage is 100%, so every parameter is already documented in the schema and the baseline is 3. The description adds some grouping value by mapping actions to their parameters (e.g., spec fields for submit, repair's rerun options, task_key for get_output), but it does not add format or syntax details beyond what the schema already carries.

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

Purpose4/5

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

The opening sentence names a specific resource (Databricks job runs) with concrete verbs (start, monitor, inspect, cancel, repair), and the bullet list enumerates every action. It is clear what the tool does, but it never names a sibling such as manage_jobs (job definitions) or manage_pipeline_run to disambiguate runs from job-level management, so it stops short of full sibling differentiation.

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

Usage Guidelines4/5

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

Each action is annotated with its intent and required inputs, and destructive actions (cancel, cancel_all, delete_run) are flagged as needing confirmation while repair is flagged EXECUTION, which tells the agent how to proceed. What is missing is explicit routing guidance against alternative tools when a user wants to manage job definitions rather than runs.

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