QPanda3 Runtime 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. It discloses key behavioral traits: it's a read operation (implied by 'get'/'check'), returns a dictionary with specific fields (status, task_id, task_status, progress, error_message), and includes possible task_status values. It does not cover aspects like rate limits, authentication needs, or error handling beyond the status, but provides substantial context for a status-checking tool.

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 a clear purpose statement, Args and Returns sections, and an example. It is appropriately sized and front-loaded, with the key information presented first. However, the example includes conditional logic that, while useful, adds slight verbosity, preventing a perfect score of 5.

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 (simple status check), no annotations, and the presence of an output schema (implied by the Returns section), the description is complete enough. It explains the purpose, parameter semantics, return values, and usage context, covering all necessary aspects for an AI agent to invoke it correctly without redundancy.

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?

The input schema has 0% description coverage, so the description must compensate. It adds meaning by explaining that 'task_id' is 'The ID of the task (returned by sample_tool or estimate_tool)', which clarifies the parameter's origin and usage. This is helpful beyond the schema's type information, but does not detail format constraints or examples, keeping it from a 5.

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: 'Get the execution status of a task' and 'Check the current status of a submitted quantum computing task.' It specifies the verb ('get'/'check') and resource ('task status'), but does not explicitly differentiate from sibling tools like 'list_my_tasks_tool' or 'cancel_task_tool', which prevents a score of 5.

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 clear context for when to use this tool: after submitting a task via 'sample_tool' or 'estimate_tool', as indicated in the Args section. It also hints at an alternative action ('get_task_results_tool') in the example when status is 'DONE'. However, it does not explicitly state when not to use it or compare to all relevant siblings like 'list_my_tasks_tool', so it falls short of a 5.

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