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OriginQ

QPanda3 Runtime MCP Server

by OriginQ

batch_estimate_tool

Execute batch expectation estimation with a single observable to compare multiple quantum circuits against the same metric using the QPanda3 Runtime MCP Server.

Instructions

Execute batch expectation estimation with a single observable.

Submit multiple quantum circuits for expectation value estimation with a single observable. This is the "n circuits + 1 observable" pattern, useful for comparing different circuit strategies against the same metric.

Args: circuits: List of quantum circuits in QProg/QCircuit/OriginIR format. Note: Circuits should NOT contain measurement operations. observable: Observable to measure for all circuits. Supports: - Dict format: {"Z0 Z1": 0.5, "X0": -0.3} device_id: Target device ID (e.g., '20').

Returns: Dictionary containing: - status: "success" or "error" - task_id: ID for tracking the task - device_id: Target device - num_circuits: Number of circuits in the batch - message: Status message

Example: result = batch_estimate_tool( circuits=[ "QINIT 2\nCREG 2\nH q[0]\nCNOT q[0],q[1]", "QINIT 2\nCREG 2\nX q[0]\nCNOT q[0],q[1]" ], observable={"Z0 Z1": 1.0}, device_id="20" )

Note: Use get_task_status_tool and get_task_results_tool to check progress and retrieve results.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
circuitsYes
observableYes
device_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations provided, the description carries full burden and does well. It explains the asynchronous nature (returns task_id, mentions companion status/result tools), specifies format requirements ('Circuits should NOT contain measurement operations'), and describes the return structure. It doesn't mention rate limits, authentication needs, or error conditions, keeping it from a perfect score.

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 efficiently structured with clear sections: purpose statement, parameter explanations, return documentation, example, and usage notes. Every sentence adds value without redundancy. The information is front-loaded with the core purpose immediately stated.

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

Completeness5/5

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

Given the tool's complexity (batch quantum computation), no annotations, and 0% schema coverage, the description provides excellent completeness. It covers purpose, parameters, returns, example usage, and follow-up actions. The output schema exists, so the description appropriately focuses on explaining the semantics rather than repeating structural details.

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

Parameters5/5

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 parameter semantics. It explains what 'circuits' should contain (formats, exclusion of measurements), shows 'observable' format options with examples, and clarifies 'device_id' purpose. The example further illustrates proper usage of all three parameters.

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 specific action ('Execute batch expectation estimation'), resource ('with a single observable'), and pattern ('n circuits + 1 observable'). It distinguishes this tool from potential siblings by explaining this is for comparing different circuit strategies against the same metric, unlike single-circuit estimation tools.

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

Usage Guidelines5/5

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

The description provides explicit usage guidance: 'useful for comparing different circuit strategies against the same metric' tells when to use it. It also distinguishes from alternatives by noting this is the 'n circuits + 1 observable' pattern. The Note section explicitly mentions companion tools for checking progress and retrieving results.

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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