Quantum Code Validator
An MCP server that provides static validation and documentation lookup for quantum computing libraries, currently supporting PennyLane. It allows users to verify quantum code syntax and method usage against official library documentation to ensure correct implementation.
README
Quantum Code Validator
A Model Context Protocol (MCP) server for validating quantum computing library code. Currently supports PennyLane, with plans to expand support for other quantum computing libraries in the future.
Overview
The Quantum Code Validator is a tool that helps developers ensure their quantum computing code is valid and follows the correct usage patterns for various quantum libraries. It provides static validation of quantum code against official documentation and offers reference documentation lookup capabilities.
Features
- Static Code Validation: Validates quantum code by:
- Checking code syntax using Python's
astmodule - Verifying code compilation using
py_compile - Comparing quantum library method usage against official documentation
- Checking code syntax using Python's
- Reference Documentation Lookup: Retrieves method documentation for specific versions of quantum libraries
- Version-Specific Validation: Supports validation against specific versions of quantum libraries
- Extensible Architecture: Designed to support multiple quantum computing libraries
Current Support
- PennyLane: Full support for static validation and reference documentation lookup
- Supported versions: v0.35.0 - v0.41.1
Planned Support
The following quantum computing libraries are planned for future support:
- Qulacs
- Qiskit
- Cirq
- cuQuantum
- And more...
Usage
The server provides two main tools:
-
validate_quantum_method_by_static:# Example usage result = validate_quantum_method_by_static( code="your_quantum_code_here", version="v0.41.1" # Optional ) -
request_quantum_method_reference:# Example usage docs = request_quantum_method_reference( method_name="qml.CNOT", # Method name without arguments version="v0.41.1" # Optional )
Installation
1. Install with uv
git clone https://github.com/yourusername/quantum-code-validator.git
cd quantum-code-validator
-
Create and activate a virtual environment:
python -m venv .venv source .venv/bin/activate # On Windows: .venv\Scripts\activate -
Install uv (if not already installed):
curl -LsSf https://astral.sh/uv/install.sh | sh -
Install dependencies using uv:
uv sync -
Run the server:
uv run src/server.py
<!--
2. Install with Docker
-
Set the required environment variables (for downloading reference documents from Google Cloud Storage):
GOOGLE_CREDENTIALS_JSON: Service account JSON stringGCS_BUCKET_NAME: GCS bucket nameGCS_PREFIX: (Optional) Prefix within the bucket
-
Build and run the container:
docker build -t quantum-code-validator . docker run -p 8000:8000 \ -e GOOGLE_CREDENTIALS_JSON='...' \ -e GCS_BUCKET_NAME='your-bucket' \ -e GCS_PREFIX='your/prefix' \ quantum-code-validatorYou can pass
GOOGLE_CREDENTIALS_JSONdirectly as a string or use a.envfile with the--env-fileoption. -
The server will start on port 8000 by default. -->
Setting MCP Server
1. Local MCP Server by uv
1.1 Load and Parse PennyLane Source Code
Load basic information from PennyLane's source code and save it as JSON files by version. The PennyLane version will be the one installed in the execution environment, so please switch it using the uv command as needed.
uv run scripts/parse_pennylane_api.py ./refdocs/pennylane/raw/v0.41.1.json
1.2 Format Source Code to Document
Next, we will use an LLM to format the basic information extracted in Step 1 into document information that can be accessed on MCP. Please specify the PennyLane versions to be converted into documents as a comma-separated list. Note that this process uses an LLM, so the "OPENAI_API_KEY" environment variable must be set, and there is a cost of approximately $2.50 per version. The formatting results will be saved in "./refdocs/pennylane/formatted".
uv run scripts/format_docs_by_llm.py v0.41.0,v0.41.1
1.3 Setup MCP Server on Local
Finally, by configuring the mcp.json file according to the platform and starting the MCP server, the tool becomes available for use with the target tool. As a reference, a link to the documentation on how to configure it for Claude Desktop is provided.
{
"mcpServers": {
"quantum-code-validator": {
"command": "uv",
"args": [
"--directory",
"/your/mcp/server/directory/quantum-code-validator",
"run",
"server.py",
"--transport",
"stdio"
]
}
}
}
<!--
2. Use Remote Server
Although Claude Desktop does not support MCP servers launched remotely, tools such as Cline or Cursor can connect to MCP via a remote server using the following configuration.
{
"mcpServers": {
"quantum-code-validator": {
"url": "https://quantum-code-validator.onrender.com/sse"
}
}
}
-->
License
This project is licensed under the MIT License - see the LICENSE file for details.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.