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 of behavioral disclosure. It effectively describes the asynchronous nature of the task (returns a task ID for tracking, requires separate tools for progress/results), specifies circuit constraints ('should NOT contain measurement operations'), and outlines the return structure. However, it doesn't mention potential rate limits, authentication requirements, or error conditions beyond the status field.

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 clear sections (purpose, args, returns, example, note) and front-loaded with the core functionality. While comprehensive, some sentences could be more concise (e.g., the mathematical notation could be simplified). Overall, it efficiently conveys necessary information without significant waste.

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 (quantum computation with asynchronous execution), no annotations, and an output schema that documents the return structure, the description provides complete context. It covers purpose, parameters, usage flow with sibling tools, constraints, and includes a practical example. The output schema handles return value details, so the description appropriately focuses on operational guidance.

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?

With 0% schema description coverage, the description fully compensates by providing detailed semantics for all three parameters. It explains the 'circuit' parameter accepts single circuits or lists in specific formats, the 'observable' parameter accepts dict or Pauli string formats with examples, and 'device_id' is clearly identified as a target device identifier. The example further clarifies usage.

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 specific action ('execute expectation estimation task'), the resource ('quantum circuit'), and the mathematical operation ('computes the expectation value ⟨ψ|O|ψ⟩'). It distinguishes from siblings like 'batch_estimate_tool' by focusing on single/batch execution rather than purely batch operations, and from 'sample_tool' by computing expectation values rather than sampling measurements.

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 guidance on when to use this tool versus alternatives: it names 'get_task_status_tool' to check progress and 'get_task_results_tool' to retrieve results after completion. It also implicitly distinguishes from 'batch_estimate_tool' by handling both single and batch circuits, and from 'estimate_with_binding_tool' by not requiring pre-bound observables.

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