siemens-graph-studio-mcp-server

# RMGS MCP Server An MCP (Model Context Protocol) server for **Altair Graph Studio (AGS)** that provides intelligent SPARQL query capabilities, ontology management, and graphmart construction tools — all accessible from AI coding assistants like GitHub Copilot, Claude Code, and other MCP-compatible clients. ## Features - **SPARQL Query Execution** — Run SPARQL queries against AGS graphmarts - **Knowledge Discovery** — Explore ontologies, classes, and properties - **Ontology Management** — Create, modify, and delete ontologies - **Graphmart Construction** — Build and manage transformation layers and steps - **Agent Memory** — Persistent and ephemeral memory for context retention - **Dual Modes** — `explore` (read-only, 13 tools) and `create` (all 51 tools) - **Multiple Transports** — stdio, SSE, and streamable-http ## Installation ```bash pip install siemens-graph-studio-mcp-server ``` ### Default setup (recommended) Use this as the baseline setup when installing from PyPI. 1. Create a Python virtual environment: ```bash python -m venv graph_mcp_env ``` 2. Activate the environment: ```bash source /.../howtomcp/graph_mcp_env/bin/activate ``` 3. Install from PyPI: ```bash pip install siemens-graph-studio-mcp-server ``` 4. Start MCP as two separate servers (recommended): ```bash siemens-graph-studio-mcp-server --config ags-config.json --mode explore --transport sse --port 8000 siemens-graph-studio-mcp-server --config ags-config.json --mode create --transport sse --port 8001 ``` > **Note:** This also installs `rdflib`, which adds `rdfpipe` and `rdfgraphisomorphism` executables to your environment. These are standard rdflib CLI utilities and can be safely ignored. ### From Source ```bash git clone <repository-url> cd rmgs-mcp-server pip install -e . ``` --- ## Quick Start ### 1. Create a config file Save this as `ags-config.json` (e.g., in your home directory or project): ```json { "servers": { "my-server": { "host": "your-ags-server.ex…