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OriginQ

QPanda3 Runtime MCP Server

by OriginQ

cancel_task_tool

Cancel running or pending quantum computing tasks on the QPanda3 Runtime MCP Server by providing a task ID to stop execution before completion.

Instructions

Cancel a running or pending task.

Request cancellation of a submitted task. This is only effective if the task has not yet completed.

Args: task_id: The ID of the task to cancel.

Returns: Dictionary containing: - status: "success" or "error" - task_id: The task ID - message: Cancellation status

Example: cancel_task_tool("task_12345")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
task_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations, the description carries the full burden. It discloses that cancellation is a request (not guaranteed) and only works on incomplete tasks, adding useful behavioral context. However, it does not cover aspects like permissions, rate limits, or error handling beyond the return structure.

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 well-structured with a purpose statement, usage condition, parameter explanation, return details, and an example—all in a compact format. Every sentence adds value without redundancy, and it is front-loaded with the core action.

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 complexity (mutation with no annotations), the description is mostly complete: it explains purpose, usage, parameters, and returns. The output schema exists, so return values are documented. However, it lacks details on side effects or error cases, leaving minor gaps.

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

Parameters4/5

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

Schema description coverage is 0%, but the description compensates by explaining the single parameter 'task_id' as 'The ID of the task to cancel', adding clear meaning. It does not provide format details (e.g., string pattern), but the example helps. With 0% coverage and 1 parameter, this is above the baseline.

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 the verb ('cancel') and resource ('a running or pending task'), distinguishing it from sibling tools like get_task_status_tool or get_task_results_tool. It specifies the action is a request for cancellation, not an immediate termination.

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

Usage Guidelines4/5

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

It provides explicit context on when to use it ('only effective if the task has not yet completed'), but does not mention alternatives like waiting for completion or using other task management tools. The guidance is clear but lacks sibling tool comparisons.

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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