RFQ_SIEMENS_Teamcenter_MCP
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
Teamcenter Phase 1 Workflow Skeleton
This repository now contains a modular Phase 1 demonstration scaffold for the upload-to-Teamcenter-draft workflow.
What is included
Existing Teamcenter FastAPI wrapper and MCP bridge preserved
New Phase 1 workflow API under
/api/*SQLAlchemy-backed workflow persistence with SQLite default and PostgreSQL-ready configuration
Deterministic mock Teamcenter mode for demos
File validation, extraction, similarity search, BQ drafting, document generation, approval, and save flow
Vite React frontend scaffold in
frontend/Demo data and sample upload fixtures in
shared/demo/
Related MCP server: Suppr MCP
Demo credentials
viewer / viewerengineer / engineer
Local backend startup
Create or activate a Python 3.11 environment.
Install dependencies:
pip install -r requirements.txtCopy
.env.exampleto.envand adjust values if needed.Run the API:
python -m uvicorn app.main:app --reload
The API will initialize demo users and the local SQLite database automatically on startup.
Frontend startup
cd frontendnpm installnpm run dev
Docker startup
docker compose up --build
Demo happy path
Log in as
engineer.Upload
shared/demo/uploads/requirement_sample.txtin mock mode.Call:
POST /api/workflows/{id}/validate-filesPOST /api/workflows/{id}/extractPUT /api/workflows/{id}/requirement-reviewPOST /api/workflows/{id}/similarity-searchPOST /api/workflows/{id}/bq-selectionPUT /api/workflows/{id}/bq-draftPOST /api/workflows/{id}/bq-preparePOST /api/workflows/{id}/documents/generatePOST /api/workflows/{id}/approvePOST /api/workflows/{id}/save-teamcenter
Tests
Backend and contract tests live under tests/.
Related MCP Connectors
Design and revise PCBs from plain language, track jobs, and retrieve design artifacts.
Convert Revit files to XKT, IFC, or DWG and query BIM data via natural language.
Human-in-the-loop document extraction: submit a doc, get validated fields and an audit trail.
Turn documents into structured, AI-ready data by parsing, enriching, chunking, and embedding.
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