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Qlik MCP Server

by undsoul

qlik_automl_get_deployment

Retrieve deployment details for automated machine learning models in Qlik Cloud. Use this tool to access configuration, status, and performance metrics of deployed ML models.

Instructions

Get deployment details

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
deploymentIdYesDeployment ID
Behavior1/5

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. 'Get deployment details' implies a read-only operation but does not specify permissions required, rate limits, error conditions, or what the output contains (e.g., deployment status, model metrics). This leaves critical behavioral traits undocumented.

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 with only three words, front-loading the core action. There is no wasted language or unnecessary elaboration, making it efficient in structure despite its informational shortcomings.

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 no annotations and no output schema, the description is incomplete for a tool that likely returns detailed deployment information. It fails to explain what 'details' entail, potential response formats, or error handling. For a tool with one parameter but unknown behavioral complexity, this minimal description is inadequate.

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 description coverage is 100%, with the single parameter 'deploymentId' documented in the schema. The description does not add any meaning beyond the schema, such as explaining where to find the deployment ID or its format. Baseline score of 3 applies since the schema adequately covers parameter semantics.

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

Purpose2/5

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

The description 'Get deployment details' restates the tool name 'qlik_automl_get_deployment' almost verbatim, making it tautological. It lacks specificity about what 'deployment' refers to (e.g., AutoML model deployment) or what 'details' include, failing to distinguish it from sibling tools like 'qlik_automl_list_deployments' or 'qlik_automl_get_experiment'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

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. It does not mention prerequisites (e.g., needing a deployment ID), exclusions, or related tools like 'qlik_automl_list_deployments' for listing deployments first. The description offers no context for usage decisions.

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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