Qlik MCP Server

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's two-step flow, error conditions (405 error), and prerequisites (e.g., 'requires Qlik Answers enabled' for Option B). However, it lacks details on rate limits, authentication needs, or response formats, which are important for a tool with complex interactions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with sections for flow, options, examples, and error handling, making it easy to follow. It is appropriately sized for a complex tool, but some sentences could be more concise (e.g., the example section is detailed but slightly verbose). Overall, it front-loads key information effectively.

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

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

Given the tool's complexity (5 parameters, no output schema, no annotations), the description does a good job of explaining usage, flow, and error handling. It compensates for the lack of output schema by describing expected returns in examples (e.g., 'Returns model with fields' and 'Returns actual data'). However, it could benefit from more details on response formats or limitations to be fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description adds significant value by explaining the semantics of parameters in the context of the two-step flow (e.g., 'text' + 'appId' for Step 1, 'refinedQuestion' + 'appId' for Step 2) and clarifying usage scenarios (e.g., 'conversationId' for multi-app selection). This goes beyond the schema's basic descriptions, though it doesn't cover all parameters equally.

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

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

The description clearly states the tool's purpose: 'Ask natural language questions about Qlik data using Insight Advisor.' It specifies the verb ('ask'), resource ('Qlik data'), and method ('using Insight Advisor'), distinguishing it from sibling tools like qlik_search or qlik_answers_ask_question by focusing on the Insight Advisor feature for natural language queries.

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

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage guidelines with two options (A and B), including when to use each based on appId availability and error handling (e.g., 'If 405 error, use Option A instead'). It also references an alternative tool (qlik_search_apps) for finding app IDs, offering clear alternatives and context for decision-making.

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