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ChrisChoTW

databricks-mcp

by ChrisChoTW

list_pipeline_updates

Retrieve the update history for a Databricks pipeline to monitor changes and track modifications over time.

Instructions

List pipeline update history

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pipeline_idYes
limitNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The handler implementation for the list_pipeline_updates tool.
    @mcp.tool
    def list_pipeline_updates(ctx: Context, pipeline_id: str, limit: int = 10) -> List[Dict[str, Any]]:
        """List pipeline update history"""
        w = get_workspace_client()
        updates = w.pipelines.list_pipeline_events(pipeline_id=pipeline_id, max_results=limit)
        return [u.as_dict() for u in updates]
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('list') but doesn't clarify if this is a read-only operation, what permissions might be required, whether results are paginated, or any rate limits. For a tool with no annotations, this leaves significant gaps in understanding its behavior.

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 a single, clear sentence with no wasted words. It's front-loaded with the core action and resource, making it easy to parse quickly. Every word earns its place by conveying essential information without redundancy.

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

Completeness3/5

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

Given that an output schema exists, the description doesn't need to explain return values. However, with no annotations, 0% schema description coverage, and multiple sibling tools, the description is minimal. It covers the basic purpose but lacks details on usage, parameters, or behavior, making it adequate but incomplete for effective tool selection.

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?

The input schema has 0% description coverage, so parameters 'pipeline_id' and 'limit' are undocumented in the schema. The description doesn't add any semantic details about these parameters, such as what 'pipeline_id' refers to or how 'limit' affects the output. However, with only 2 parameters and a default for 'limit', the baseline is 3 as the schema provides basic structure.

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

Purpose3/5

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

The description 'List pipeline update history' clearly states the verb ('list') and resource ('pipeline update history'), making the purpose understandable. However, it doesn't distinguish this tool from sibling tools like 'list_pipelines' or 'get_pipeline', leaving ambiguity about what specifically differentiates pipeline updates from pipelines themselves.

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. With sibling tools like 'list_pipelines' and 'get_pipeline' available, there's no indication of whether this tool is for historical changes, recent updates, or a specific subset of pipeline data, leaving the agent to guess based on the name alone.

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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