---

name: qiskit-mcp-dev description: Guide development of Qiskit MCP servers. Use when creating tools, resources, tests, or new servers. Helps with async patterns, pytest fixtures, GitHub CI/CD setup, and code quality. Trigger on "add tool", "add resource", "write test", "new server", "FastMCP", "MCP development", or "GitHub workflow". allowed-tools: Read, Grep, Glob, Edit, Write, Bash

Qiskit MCP Server Development Guide

Instructions

When helping with MCP server development in this repository:

1. Read existing code first - Check server.py and core modules before adding new functionality
2. Follow established patterns - Match the style of existing tools and tests
3. Include license headers - All new files need the Apache 2.0 header
4. Keep tools async - MCP tools use async def, delegate to core module functions
5. Mock external services - Never call real IBM Quantum APIs in tests
6. Update GitHub CI/CD - New servers require workflow updates (see below)

License Header (Required for all new files)

Copy# This code is part of Qiskit.
#
# (C) Copyright IBM 2025.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.

Adding a Tool

Tools go in server.py and delegate to async functions in the core module:

Copy# Example: from qiskit_<name>_mcp_server.<core> import my_function
from qiskit_ibm_runtime_mcp_server.ibm_runtime import my_function

@mcp.tool()
async def my_tool_name(param: str, optional_param: int = 10) -> dict[str, Any]:
    """Clear description of what this tool does.

    Args:
        param: Description of the parameter
        optional_param: Description with default behavior

    Returns:
        Description of the return structure
    """
    return await my_function(param, optional_param)

Key patterns:

• Tool function name ends with _tool suffix
• Docstring describes the tool for AI assistants
• Return type is dict[str, Any]
• Delegate to core module, don't implement logic here

Error Handling Pattern

All tools return consistent response format:

Copy# Success
return {"status": "success", "data": result, ...}

# Error
return {"status": "error", "message": "Description of what went wrong"}

Adding a Resource

Copy@mcp.resource("protocol://path", mime_type="application/json")
async def get_resource_name() -> dict[str, Any]:
    """Description of the resource."""
    return await get_data_from_core_module()

Writing Tests

Tests use pytest with mocked services. See tests/conftest.py for fixtures.

Copyimport pytest
from unittest.mock import Mock, patch

@pytest.mark.asyncio
async def test_my_tool(mock_service):
    """Test description."""
    with patch(
        "qiskit_<name>_mcp_server.<core>.ExternalService",
        return_value=mock_service,
    ):
        result = await my_function()
        assert result["status"] == "success"

Common fixture patterns in conftest.py (names vary by server):

• mock_*_service - Mocked external service (e.g., mock_runtime_service, mock_http_client)
• mock_env_vars - Sets environment variables like QISKIT_IBM_TOKEN
• reset_* - Resets global state between tests (often autouse=True)

Code Quality

Run from the server directory:

Copyuv run ruff format src/ tests/    # Format
uv run ruff check src/ tests/     # Lint
uv run mypy src/                   # Type check
uv run pytest                      # Test

Adding a New Server

When creating a new server qiskit-<name>-mcp-server:

1. Create server directory with standard structure (see below)
2. Add to workspace in root pyproject.toml:

   Copy[tool.uv.workspace]
   members = [..., "qiskit-<name>-mcp-server"]
   

3. Create examples/ with LangChain agent demos:
   • README.md - Setup instructions and usage
   • langchain_agent.ipynb - Interactive Jupyter tutorial
   • langchain_agent.py - CLI agent supporting multiple LLM providers
4. Update GitHub CI - .github/workflows/test.yml:
   • Add to lint job (install, ruff, mypy, bandit steps)
   • Add new test-<name> job
5. Update GitHub CD - .github/workflows/publish-pypi.yml:
   • Add to workflow_dispatch options
   • Add publish-<name> job
   • Add to publish-meta-package needs array
6. Update documentation - README.md, AGENTS.md

Server Structure

Copyqiskit-<name>-mcp-server/
├── src/qiskit_<name>_mcp_server/
│   ├── __init__.py      # Package init, version
│   ├── server.py        # FastMCP server, @mcp.tool() and @mcp.resource()
│   └── <core>.py        # Async business logic
├── tests/
│   ├── conftest.py      # Fixtures for mocking
│   └── test_*.py        # Test files
├── examples/
│   ├── README.md                # Example documentation
│   ├── langchain_agent.ipynb    # Jupyter notebook tutorial
│   └── langchain_agent.py       # CLI agent example
├── pyproject.toml
├── pytest.ini           # (optional) pytest configuration
├── .env.example         # (optional) Environment variable template
├── LICENSE              # Apache 2.0 license (copy from root)
├── README.md
└── run_tests.sh

Quick Reference

For comprehensive documentation, read AGENTS.md in the repository root.