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JustTryAI

Databricks MCP Server

list_jobs

Retrieve and display all Databricks jobs to monitor and manage scheduled workflows and automated tasks.

Instructions

List all Databricks jobs

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Implementation Reference

  • MCP tool handler for 'list_jobs': registers the tool and implements the execution logic by calling the jobs API and formatting the response as TextContent.
    @self.tool(
        name="list_jobs",
        description="List all Databricks jobs",
    )
    async def list_jobs(params: Dict[str, Any]) -> List[TextContent]:
        logger.info(f"Listing jobs with params: {params}")
        try:
            result = await jobs.list_jobs()
            return [{"text": json.dumps(result)}]
        except Exception as e:
            logger.error(f"Error listing jobs: {str(e)}")
            return [{"text": json.dumps({"error": str(e)})}]
  • Core helper function that performs the actual Databricks API call to list jobs using make_api_request.
    async def list_jobs() -> Dict[str, Any]:
        """
        List all jobs.
        
        Returns:
            Response containing a list of jobs
            
        Raises:
            DatabricksAPIError: If the API request fails
        """
        logger.info("Listing all jobs")
        return make_api_request("GET", "/api/2.0/jobs/list")
Behavior2/5

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. 'List all Databricks jobs' implies a read-only operation, but it doesn't specify whether this requires authentication, returns paginated results, includes metadata like job status or owner, or has any rate limits. For a listing tool with zero annotation coverage, this leaves significant behavioral 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 extremely concise at just four words, front-loading the essential information with zero wasted words. Every word earns its place by specifying the action ('List'), scope ('all'), resource type ('Databricks jobs'), making it efficiently structured despite its brevity.

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 complexity of a job listing operation with no annotations, no output schema, and 0% parameter documentation, the description is incomplete. It doesn't explain what information is returned (e.g., job IDs, names, statuses), how results are formatted, or any constraints. For a tool that likely returns structured data about jobs, this minimal description leaves too many questions unanswered.

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

Parameters2/5

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

The input schema has 0% description coverage with one required parameter 'params' as an object type. The description provides no information about what parameters are accepted (e.g., filters for job name, status, owner, or pagination controls). Since schema coverage is 0%, the description should compensate but fails to do so, leaving parameters completely undocumented.

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 description clearly states the verb ('List') and resource ('all Databricks jobs'), making the tool's purpose immediately understandable. It distinguishes this from sibling tools like 'run_job' or 'create_cluster' by focusing on listing rather than execution or creation. However, it doesn't specify if this includes archived, active, or all job states, which would make it a perfect 5.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention whether to use 'list_jobs' versus 'run_job' for job management, or how it relates to other listing tools like 'list_clusters' or 'list_notebooks'. There's no context about prerequisites, permissions needed, or typical use cases.

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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