Authenticated MCP server for Teamcenter Knowledge Base APIs with Azure AD support
Project description
Teamcenter MCP Server
Universal MCP server for integrating AI assistants with Teamcenter Knowledge Base APIs.
📦 Live on PyPI: https://pypi.org/project/teamcenter-mcp-server-test/
Quick Start (Just Copy & Paste)
Continue.dev
Add to ~/.continue/config.json:
{
"experimental": {
"modelContextProtocolServers": [{
"transport": {
"type": "stdio",
"command": "uvx",
"args": ["teamcenter-mcp-server-test", "--base-url", "http://localhost:8000"]
}
}]
}
}
VS Code
Add to .vscode/mcp.json:
{
"servers": {
"teamcenter": {
"type": "stdio",
"command": "uvx",
"args": ["teamcenter-mcp-server-test", "--base-url", "http://localhost:8000"]
}
}
}
JetBrains IDEs
Add to ~/.mcp.json:
{
"mcpServers": {
"teamcenter": {
"command": "uvx",
"args": ["teamcenter-mcp-server-test", "--base-url", "http://localhost:8000"]
}
}
}
Usage
→ See USAGE.md for copy & paste examples ←
Quick examples:
- VS Code:
@workspace get Teamcenter API documentation for part creation - Continue.dev:
@MCP search for PLM workflow integration documentation
Production Setup
Replace http://localhost:8000 with your real Teamcenter API:
"args": ["teamcenter-mcp-server-test", "--base-url", "https://teamcenter.yourcompany.com"]
Testing
Quick Test
uvx teamcenter-mcp-server-test --version
Demo/Development Setup
Start mock API server:
git clone https://github.com/your-repo/mock-api
cd mock-api
uv run uvicorn main:app --reload
Server runs on http://localhost:8000 - use this URL in configs above.
Development (Advanced)
Click for development setup
Installation
curl -LsSf https://astral.sh/uv/install.sh | sh
Build Package
uv build
Run Tests
uv run pytest tests/ -v
Publishing to PyPI
→ See DEVELOPER.md for release instructions ←
Files Overview
auth_mcp_stdio.py: Main MCP servermain.py: Mock API server for developmentpyproject.toml: Package configuration
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