qiskit-code-assistant-mcp-server

MCP server for Qiskit Code Assistant
Components
Tools
The server implements four tools:
accept_model_disclaimer_tool: Accept the disclaimer for a given model
get_completion_tool: Get completion for a given prompt
get_rag_completion_tool: Get RAG completion for answering conceptual or descriptive questions about Qiskit or Quantum
accept_completion_tool: Accept a given completion
Prerequisites
- Python 3.10 or higher
- uv package manager (recommended)
- IBM Quantum account and API token
- Access to Qiskit Code Assistant service
Installation
Install from PyPI
The easiest way to install is via pip:
pip install qiskit-code-assistant-mcp-server
Install from Source
This project uses uv for virtual environments and dependencies management. If you don't have uv installed, check out the instructions in https://docs.astral.sh/uv/getting-started/installation/
Setting up the Project with uv
Configuration
Environment Variables
The server can be configured using environment variables in your .env file:
QISKIT_IBM_TOKEN - Your IBM Quantum API token (required)
QCA_TOOL_API_BASE - Qiskit Code Assistant API base URL (default: https://qiskit-code-assistant.quantum.ibm.com)
QCA_TOOL_MODEL_NAME - Default model name (default: mistral-small-3.2-24b-qiskit)
QCA_REQUEST_TIMEOUT - Request timeout in seconds (default: 30.0)
QCA_MCP_DEBUG_LEVEL - Logging level: DEBUG, INFO, WARNING, ERROR, CRITICAL (default: INFO)
Model Selection
The server includes an automatic model availability guardrail that:
- Checks available models from the Qiskit Code Assistant service at startup
- Uses the configured
QCA_TOOL_MODEL_NAME if available
- Automatically falls back to the first available model if the default is unavailable
- Logs warnings when using a fallback model
- Gracefully handles API errors by using the configured default
This ensures the server can start and function even when the default model is temporarily unavailable.
Quick Start
Running the Server
uv run qiskit-code-assistant-mcp-server
The server will start and listen for MCP connections.
Synchronous Usage
For frameworks that don't support async operations, all async functions have a .sync attribute for synchronous execution:
from qiskit_code_assistant_mcp_server.qca import (
get_completion,
get_rag_completion,
list_models
)
result = get_completion.sync("Write a quantum circuit for a Bell state")
print(result)
rag_result = get_rag_completion.sync("What is quantum entanglement?")
print(rag_result)
models = list_models.sync()
print(models)
Available functions (all support .sync):
list_models() - List available models
get_model(model_id) - Get model info
get_completion(prompt) - Get code completion
get_rag_completion(prompt) - Get RAG-based completion
accept_completion(completion_id) - Accept a completion
get_service_status() - Get service status
LangChain Integration Example:
Note: To run LangChain examples you will need to install the dependencies:
pip install langchain langchain-mcp-adapters langchain-openai python-dotenv
import asyncio
import os
from langchain.agents import create_agent
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_mcp_adapters.tools import load_mcp_tools
from langchain_openai import ChatOpenAI
from dotenv import load_dotenv
load_dotenv()
async def main():
mcp_client = MultiServerMCPClient({
"qiskit-code-assistant": {
"transport": "stdio",
"command": "qiskit-code-assistant-mcp-server",
"args": [],
"env": {
"QISKIT_IBM_TOKEN": os.getenv("QISKIT_IBM_TOKEN", ""),
},
}
})
async with mcp_client.session("qiskit-code-assistant") as session:
tools = await load_mcp_tools(session)
llm = ChatOpenAI(model="gpt-5.2", temperature=0)
agent = create_agent(llm, tools)
response = await agent.ainvoke("Write a quantum circuit that creates a Bell state")
print(response)
asyncio.run(main())
For more LLM providers (Anthropic, Google, Ollama, Watsonx) and detailed examples including Jupyter notebooks, see the examples/ directory.
Testing and debugging the server
Note: to launch the MCP inspector you will need to have node and npm
-
From a terminal, go into the cloned repo directory
-
Switch to the virtual environment
source .venv/bin/activate
-
Run the MCP Inspector:
npx @modelcontextprotocol/inspector uv run qiskit-code-assistant-mcp-server
-
Open your browser to the URL shown in the console message e.g.,
MCP Inspector is up and running at http://localhost:5173
Testing
This project includes comprehensive unit and integration tests.
Running Tests
Quick test run:
./run_tests.sh
Manual test commands:
uv sync --group dev --group test
uv run pytest
uv run pytest -m "not integration"
uv run pytest -m "integration"
uv run pytest --cov=src --cov-report=html
uv run pytest tests/test_qca.py -v
Test Structure
tests/test_qca.py - Unit tests for QCA functions
tests/test_utils.py - Unit tests for utility functions
tests/test_constants.py - Unit tests for configuration
tests/test_sync.py - Unit tests for synchronous execution
tests/test_integration.py - Integration tests
tests/conftest.py - Test fixtures and configuration
Test Coverage
The test suite covers:
- ✅ All QCA API interactions
- ✅ Model selection and availability guardrail
- ✅ Error handling and validation
- ✅ HTTP client management
- ✅ Synchronous execution (
.sync methods)
- ✅ Configuration validation
- ✅ Integration scenarios
- ✅ Resource and tool handlers
Resources