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Qlik MCP Server

by undsoul

qlik_automl_get_experiments

Retrieve and list AutoML experiments from Qlik Cloud to monitor machine learning projects, with options to filter by space and paginate results.

Instructions

List AutoML experiments

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
spaceIdNoFilter by space ID
limitNoMax results (default: 50)
offsetNoPagination offset
Behavior2/5

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

With no annotations provided, the description carries full burden but offers minimal behavioral information. It doesn't disclose whether this is a read-only operation, what authentication is needed, rate limits, return format, or pagination behavior. 'List' implies reading, but explicit safety/behavior details are missing.

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 just three words, front-loading the essential purpose. There's no wasted language or unnecessary elaboration, making it efficient for quick understanding.

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?

For a list operation with 3 parameters and no annotations or output schema, the description is insufficient. It doesn't explain what an 'AutoML experiment' is in this context, how results are returned, or provide any context about the Qlik environment. The agent would need to infer too much from minimal information.

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%, providing good documentation for all three parameters (spaceId, limit, offset). The description doesn't add any parameter semantics beyond what's in the schema, so it meets the baseline of 3 without compensating or enhancing the schema information.

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 ('AutoML experiments'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'qlik_automl_get_experiment' (singular) or 'qlik_automl_list_deployments', which could cause confusion about scope.

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 about when to use this tool versus alternatives. The description doesn't mention filtering capabilities (spaceId), pagination (limit/offset), or differentiate it from related AutoML tools like 'qlik_automl_get_experiment' or 'qlik_automl_list_deployments'.

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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