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kevintalbert

Cloudera Data Visualization MCP Server

by kevintalbert

create_extract

Create a data extract job that moves data from a source dataset to a target database table, applying dimension and aggregate columns.

Instructions

Create a CDV data extract job.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
src_dataset_idYesSource dataset ID.
tgt_dataconnection_idYesTarget data connection ID.
tgt_dbnameYesTarget database name.
tgt_tablenameYesTarget table name.
dim_dataYesJSON string of dimension columns, e.g. '[{"expr":"[col]","alias":"col"}]'.
agg_dataYesJSON string of aggregate columns, e.g. '[{"expr":"sum([col])","alias":"col"}]'.
partition_dataNoJSON string of partition columns for incremental refresh (default '[]').[]
schedule_idNoOptional schedule ID to attach a job to the extract.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations, the description should disclose behavioral traits. It only states a vague action, failing to mention side effects, return value (though output schema exists), or whether the job is created and then run separately.

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, efficient sentence with no redundancy. However, it may be too terse given the tool's complexity and lack of annotations.

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?

Despite an output schema existing, the description fails to provide essential context about the tool's operation, such as what a CDV data extract is, how it integrates with other tools, or typical workflow steps.

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?

Schema coverage is 100%, so each parameter is already described. The description adds no extra meaning beyond the schema, meriting a baseline score of 3.

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 it creates a CDV data extract job, using a specific verb and resource. It distinguishes from sibling 'create_' tools by specifying 'CDV data extract', but does not elaborate on what a CDV data extract is.

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 on when to use this tool versus alternatives like run_extract or other create tools. The description lacks context on prerequisites 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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