Skip to main content
Glama

pipelines_create

Create a new Databricks pipeline with configurable name, storage, libraries, and cluster settings to automate data processing workflows.

Instructions

Create a pipeline (POST /api/2.0/pipelines).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
storageNo
configurationNo
clusterNo
filtersNo
allow_duplicate_namesNo
librariesNo
continuousNo
deploymentNo
channelNo
editionNoCORE | PRO | ADVANCED
photonNo
restart_windowNo
serverlessNo
catalogNo
targetNo
schemaNoTarget schema
data_samplingNo
expected_lifecycle_stateNoPENDING | ACTIVE | FAILED

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

Annotations indicate readOnlyHint=false, so it's a write operation. The description adds nothing beyond confirming creation. It does not disclose authentication needs, side effects, or whether the operation is idempotent.

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 a single sentence with no extraneous text, making it concise. However, it could be slightly expanded to include key parameter highlights without losing conciseness.

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

Completeness1/5

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

With 19 parameters (0 required), a low schema coverage, and no description of return values or usage context, the description is severely incomplete. It fails to provide enough information for an AI agent to correctly invoke the tool.

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 only 16%, leaving most parameters without any description in the schema. The tool description itself provides no additional meaning for any parameter, failing to compensate for the low coverage.

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

Purpose5/5

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

The description 'Create a pipeline' clearly states the action and resource, and the API endpoint confirms it's a creation operation. It distinguishes from sibling tools like pipelines_edit, pipelines_get, and pipelines_delete.

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?

No guidance is provided on when to use this tool versus alternatives, such as pipelines_edit for updates or pipelines_get for retrieval. There is no mention of prerequisites, required parameters, or conditions for use.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/inav/databricks-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server