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ChrisChoTW

databricks-mcp

by ChrisChoTW

get_job

Retrieve detailed information about a Databricks job by specifying its job ID to monitor status, configurations, and execution history.

Instructions

Get job details

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The get_job tool is defined and implemented here using the @mcp.tool decorator. It uses the workspace client to retrieve job details by job_id.
    @mcp.tool
    def get_job(ctx: Context, job_id: int) -> Dict[str, Any]:
        """Get job details"""
        w = get_workspace_client()
        return w.jobs.get(job_id=job_id).as_dict()
Behavior1/5

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

With no annotations provided, the description carries full burden for behavioral disclosure but offers none. It doesn't indicate whether this is a read-only operation, what authentication is needed, potential rate limits, error conditions, or what format/details are returned. The description fails to provide any behavioral context beyond the basic action.

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 description is extremely concise at just three words, which could be appropriate if it were more informative. However, this brevity results in under-specification rather than efficient communication. While front-loaded, it lacks the substance needed to be truly helpful.

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

Completeness2/5

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

Given the tool has an output schema (which reduces the need to describe return values), but with zero annotation coverage and 0% schema description coverage for its single parameter, the description is inadequate. For a job retrieval tool that likely returns structured data, the description should provide more context about what 'details' includes and parameter requirements.

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?

Schema description coverage is 0%, meaning the single parameter 'job_id' has no documentation in the schema. The description adds no parameter information whatsoever - it doesn't explain what 'job_id' represents, where to find it, format requirements, or valid ranges. The description completely fails to compensate for the schema's lack of parameter documentation.

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

Purpose2/5

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

The description 'Get job details' is a tautology that essentially restates the tool name 'get_job' with minimal elaboration. While it indicates a retrieval action ('Get') and target resource ('job'), it lacks specificity about what 'details' entails and doesn't distinguish this tool from sibling tools like 'get_job_run' or 'list_jobs'.

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

Usage Guidelines1/5

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

The description provides no guidance on when to use this tool versus alternatives. There's no mention of prerequisites, when-not-to-use scenarios, or comparisons to sibling tools like 'get_job_run' (for run-specific details) or 'list_jobs' (for multiple jobs). The agent receives zero usage context.

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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