teamcenter-mcp-server-test 0.1.0

Authenticated MCP server for Teamcenter Knowledge Base APIs

Teamcenter MCP Server

A complete solution for integrating AI assistants with Teamcenter Knowledge Base APIs. Includes both a mock API server for development and a deployable MCP server for production use.

What You Get

• Mock API Server: Test server that simulates Teamcenter Knowledge Base responses
• MCP Server: Production-ready server that connects AI assistants to Teamcenter APIs
• Universal Deployment: Works across all IDEs and platforms via UVX

Quick Start

1. Install UV (if not already installed)

curl -LsSf https://astral.sh/uv/install.sh | sh

2. Clone and Start Mock API Server

# Start the mock API server (for development/testing)
uv run uvicorn main:app --reload

Server runs on http://127.0.0.1:8000

3. Build MCP Server Package

# Build the deployable MCP server
uv build

Creates: dist/teamcenter_mcp_server-0.1.0-py3-none-any.whl

4. Validate Your Setup

# Run the deployment validation test
python test_mcp_contract.py

This validates that your UVX wheel deployment is ready for IDE integration.

Deployment Options

Option 1: UVX (Recommended - Universal)

Use the built wheel package anywhere:

# From the built wheel (works on any machine)
uvx --from dist/teamcenter_mcp_server-0.1.0-py3-none-any.whl teamcenter-mcp-server --base-url http://localhost:8000

Why UVX is better:

• ✅ No virtual environment conflicts (Windows/WSL compatible)
• ✅ Works anywhere (just copy the .whl file)
• ✅ No installation required (isolated execution)

Option 2: Direct Python (Development Only)

Run directly from source:

# For development/testing only
uv run python auth_mcp_stdio.py --base-url http://localhost:8000

IDE Integration

VS Code

Update .vscode/mcp.json:

{
  "servers": {
    "teamcenter": {
      "type": "stdio",
      "command": "uvx",
      "args": ["--from", "dist/teamcenter_mcp_server-0.1.0-py3-none-any.whl", "teamcenter-mcp-server", "--base-url", "http://localhost:8000"]
    }
  }
}

Continue.dev

Update ~/.continue/config.json:

{
  "experimental": {
    "modelContextProtocolServers": [{
      "transport": {
        "type": "stdio",
        "command": "uvx",
        "args": ["--from", "dist/teamcenter_mcp_server-0.1.0-py3-none-any.whl", "teamcenter-mcp-server", "--base-url", "http://localhost:8000"]
      }
    }]
  }
}

JetBrains IDEs

Add to ~/.mcp.json:

{
  "mcpServers": {
    "teamcenter": {
      "command": "uvx",
      "args": ["--from", "dist/teamcenter_mcp_server-0.1.0-py3-none-any.whl", "teamcenter-mcp-server", "--base-url", "http://localhost:8000"]
    }
  }
}

Testing

Run All Tests

uv run pytest tests/ -v

Validate Setup

# Pre-flight check - run this before configuring IDEs
python test_mcp_contract.py

What this tests:

• ✅ UVX wheel package works
• ✅ MCP server initializes correctly
• ✅ Command-line arguments work
• ✅ API server connectivity (optional)

Run this whenever you:

• Build a new wheel (uv build)
• Change server configurations
• Set up on a new machine

🚀 Production Deployment

Ready to connect to your real Teamcenter API? Replace the mock server with your production endpoint:

Replace Mock URL with Production

# Instead of: --base-url http://localhost:8000
# Use your real Teamcenter API:
--base-url https://teamcenter.yourcompany.com

Update IDE Configurations

VS Code (.vscode/mcp.json):

"args": ["--from", "dist/teamcenter_mcp_server-0.1.0-py3-none-any.whl", "teamcenter-mcp-server", "--base-url", "https://teamcenter.yourcompany.com"]

Continue.dev (~/.continue/config.json):

"args": ["--from", "dist/teamcenter_mcp_server-0.1.0-py3-none-any.whl", "teamcenter-mcp-server", "--base-url", "https://teamcenter.yourcompany.com"]

Alternative: Environment Variable

# Set once, works everywhere
export TEAMCENTER_API_URL=https://teamcenter.yourcompany.com

# Then use without --base-url:
"args": ["--from", "dist/teamcenter_mcp_server-0.1.0-py3-none-any.whl", "teamcenter-mcp-server"]

🎯 That's it! No mock server needed - connect directly to production.

Usage Examples

Once configured, ask your AI assistant:

• @MCP check if the Teamcenter knowledge base is healthy
• @MCP search for PLM workflow integration documentation
• @MCP find CAD model versioning guides

Files Overview

• auth_mcp_stdio.py: Main MCP server (single file solution)
• main.py: Mock API server for development
• test_mcp_contract.py: Deployment validation script
• pyproject.toml: Package configuration for uv build
• dist/: Built wheel packages (created by uv build)

Key Commands

Command	Purpose
uv run uvicorn main:app --reload	Start mock API server
uv build	Build MCP server package
python test_mcp_contract.py	Validate setup (run first!)
uv run pytest tests/ -v	Run all tests