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utenadev

Agent Factory MCP

by utenadev

ask-qwen

Send prompts to Qwen AI models for analysis or answers. Supports file inclusion via @ syntax and model selection.

Instructions

Execute Qwen AI to get responses. Supports model selection.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoOptional model to use (e.g., 'qwen-max', 'qwen-long'). If not specified, uses the default model.
promptYesAnalysis request. Use @ syntax to include files (e.g., '@largefile.js explain what this does') or ask general questions
Behavior2/5

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

No annotations are provided, so the description must fully convey behavioral traits. It only states 'get responses' and 'supports model selection,' lacking details on side effects, authentication, rate limits, or synchronous/asynchronous behavior.

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 short and to the point, with two sentences that waste no words. It is appropriately front-loaded.

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

Completeness4/5

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

Given the tool's simplicity (2 parameters, no nested objects, no output schema), the description is mostly complete. It could mention the return type, but the phrase 'get responses' gives adequate context.

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%, and the description merely restates that model selection is supported without adding new meaning. It does not clarify parameter usage beyond what the schema already provides.

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 clearly states that the tool executes Qwen AI to get responses, which is a specific verb-resource pair. It distinguishes well from sibling tools like Ping, Help, and register_cli_tool, which serve different purposes.

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

Usage Guidelines3/5

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

The description does not provide explicit guidance on when to use this tool versus alternatives. Usage is implied by the name and description, but no when-not or context is given.

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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