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ChrisChoTW

databricks-mcp

by ChrisChoTW

list_pipelines

Retrieve and display Delta Live Tables pipelines from Databricks to monitor data processing workflows and track pipeline status.

Instructions

List Delta Live Tables Pipelines

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The handler implementation for the list_pipelines MCP tool. It uses the workspace client to retrieve pipelines and returns them as a list of dictionaries.
    @mcp.tool
    def list_pipelines(ctx: Context, limit: int = 20) -> List[Dict[str, Any]]:
        """List Delta Live Tables Pipelines"""
        w = get_workspace_client()
        pipes = w.pipelines.list_pipelines(max_results=limit)
        return [p.as_dict() for p in pipes]
Behavior2/5

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. It states 'List' which implies a read operation, but doesn't mention pagination behavior, rate limits, authentication requirements, or what 'Delta Live Tables Pipelines' specifically entails. The description is too minimal to provide adequate behavioral context.

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, with zero wasted language. It's front-loaded with the essential action and resource, making it efficient for quick scanning.

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 this is a simple list operation with only one optional parameter and an output schema exists, the description is minimally adequate. However, with no annotations and multiple sibling tools that could cause confusion, the description should provide more context about what distinguishes this tool from alternatives.

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 description mentions no parameters at all, while the schema has one parameter (limit) with 0% description coverage. Since there's only one parameter and the description doesn't add any semantic information about it, this meets the baseline for minimal parameter documentation but doesn't compensate for the schema coverage gap.

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 ('Delta Live Tables Pipelines'), making the purpose immediately understandable. It doesn't distinguish from siblings like 'list_pipeline_updates' or 'get_pipeline', but it's specific enough to identify the target resource.

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 like 'list_pipeline_updates' or 'get_pipeline'. There's no mention of context, prerequisites, or exclusions that would help an agent choose between these related tools.

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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