Which integrations are available for this server?

Provides tools for interacting with Siemens' Altair Graph Studio (AGS), enabling SPARQL query execution, ontology management, and graphmart construction.

How do I use RMGS MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@RMGS MCP Server List all classes in the equipment ontology" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

RMGS MCP Server

by engalar

Overview Schema Related Servers Score Discussions

Python

Remote

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

Related MCP server: Logseq MCP Server

Installation

pip install siemens-graph-studio-mcp-server

Default setup (recommended)

Use this as the baseline setup when installing from PyPI.

Create a Python virtual environment:

python -m venv graph_mcp_env

Activate the environment:

source /.../howtomcp/graph_mcp_env/bin/activate

Install from PyPI:

pip install siemens-graph-studio-mcp-server

Start MCP as two separate servers (recommended):

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

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):

{
  "servers": {
    "my-server": {
      "host": "your-ags-server.example.com",
      "port": 443,
      "username": "your-username",
      "password": "${AGS_PASSWORD}",
      "graphmart_uri": "http://cambridgesemantics.com/Graphmart/your-graphmart-id",
      "default": true
    }
  }
}

Security: Use ${ENV_VAR} references for passwords — they are resolved from environment variables at startup. Never commit real passwords to config files.

2. Connect your MCP client

Choose your client below.

VS Code — GitHub Copilot (Agent Mode)

Setup

Create .vscode/mcp.json in your workspace:

{
  "servers": {
    "ags-sparql-agent": {
      "type": "stdio",
      "command": "siemens-graph-studio-mcp-server",
      "args": ["--config", "/absolute/path/to/ags-config.json"],
      "env": {
        "AGS_PASSWORD": "your-password-here"
      }
    }
  }
}

Reload VS Code (Cmd+Shift+P → "Developer: Reload Window").
Open Copilot Chat and switch to Agent mode (click the mode selector at the top of the chat panel).
The AGS tools will appear in the tool list (click the 🔧 icon to verify).

Usage

Type natural language prompts in Copilot Chat (Agent mode):

What ontologies are available in this graphmart?
Show me all classes in the equipment ontology
Run a SPARQL query to count all products
Create a new transformation layer for data linking

Copilot will automatically select and call the appropriate MCP tools.

Running from source (without pip install)

If you prefer to run directly from the cloned repo:

{
  "servers": {
    "ags-sparql-agent": {
      "type": "stdio",
      "command": "python",
      "args": [
        "/absolute/path/to/rmgs_mcp_server/ags_sparql_agent.py",
        "--config", "/absolute/path/to/ags-config.json"
      ],
      "env": {
        "AGS_PASSWORD": "your-password-here"
      }
    }
  }
}

Claude Code

Setup

Add to your project's .mcp.json (or ~/.claude.json for global config):

{
  "mcpServers": {
    "ags-sparql-agent": {
      "command": "siemens-graph-studio-mcp-server",
      "args": ["--config", "/absolute/path/to/ags-config.json"],
      "env": {
        "AGS_PASSWORD": "your-password-here"
      }
    }
  }
}

Usage

In Claude Code, the MCP tools are automatically available. Use natural language:

What data is in this knowledge graph?
Show me all properties of the Customer class
Create a linking ontology between orders and products

Claude Code will call the appropriate MCP tools to interact with your AGS graphmart.

Advanced Configuration

Multiple servers

{
  "servers": {
    "production": {
      "host": "prod-ags.example.com",
      "port": 443,
      "username": "your-username",
      "password": "${PROD_PASSWORD}",
      "graphmart_uri": "http://cambridgesemantics.com/Graphmart/prod-id",
      "default": true
    },
    "staging": {
      "host": "staging-ags.example.com",
      "port": 8443,
      "username": "your-username",
      "password": "${STAGING_PASSWORD}"
    }
  }
}

The server marked "default": true is automatically selected at startup. Switch at runtime using select_server and select_graphmart tools.

Agent configuration

{
  "servers": { "...": "..." },
  "agent_config": {
    "max_iterations": 3,
    "query_timeout": 30,
    "cache_ontologies": true,
    "ontology_cache_ttl": 86400
  }
}

Transport options

# SSE (default)
siemens-graph-studio-mcp-server --config ags-config.json --transport sse --port 8000

# stdio (used by VS Code and Claude Code)
siemens-graph-studio-mcp-server --config ags-config.json

# Streamable HTTP
siemens-graph-studio-mcp-server --config ags-config.json --transport streamable-http --port 8000

Modes

# Explore mode — read-only tools only (safe for browsing)
siemens-graph-studio-mcp-server --config ags-config.json --mode explore

# Create mode — all tools including write operations (default)
siemens-graph-studio-mcp-server --config ags-config.json --mode create

Explore mode tools (13): list_servers, select_server, list_graphmarts, select_graphmart, execute_sparql_query, discover_knowledge_overview, discover_available_ontologies, discover_ontology_classes, discover_class_data_properties, discover_class_object_properties, list_ontology_imports, initialize_agent_memory, read_agent_memory

Create mode includes all 51 tools (explore + write operations).

Legacy: Direct ANZO_* environment variables (without a config file) are still supported but deprecated.

Tool Categories

System & Monitoring

Tool	Description
`test_system_connection`	Test MCP server and AGS agent status
`get_session_logs`	Get session logs and interaction history
`list_servers`	List all configured AGS servers
`select_server`	Switch to a different AGS server at runtime
`list_graphmarts`	List all graphmarts on the active server
`select_graphmart`	Switch to a different graphmart at runtime

SPARQL Query Execution

Tool	Description
`execute_sparql_query`	Execute SPARQL directly against graphmart
`query_ags_configuration`	Query graphmart metadata (local volume)
`update_ags_configuration`	Update graphmart metadata with SPARQL

Knowledge Discovery

Tool	Description
`discover_knowledge_overview`	Get overview of available knowledge
`discover_available_ontologies`	List all available ontologies
`discover_ontology_classes`	List classes in a specific ontology
`discover_class_data_properties`	List data properties for a class
`discover_class_object_properties`	List object properties for a class

Ontology Management

Tool	Description
`create_ontology`	Create a new ontology
`delete_ontology`	Delete an ontology
`register_ontology`	Register ontology with graphmart
`load_ontology_from_file`	Load TTL files into named graphs
`add_ontology_class`	Add a class to an ontology
`remove_ontology_class`	Remove a class from an ontology
`add_ontology_property`	Add a property to an ontology
`remove_ontology_property`	Remove a property from an ontology
`add_ontology_import`	Add an import to an ontology
`remove_ontology_import`	Remove an import from an ontology
`list_ontology_imports`	List imports of an ontology
`list_ontology_structure_classes`	List classes in ontology structure
`list_ontology_structure_properties`	List properties in ontology structure
`get_ontology_cache_status`	Get ontology cache status
`clear_ontology_cache`	Clear ontology caches
`refresh_ontology_cache`	Force cache refresh

Graphmart Construction & Management

Tool	Description
`create_transformation_layer`	Create transformation layers
`update_transformation_layer`	Update layer properties
`delete_transformation_layer`	Delete transformation layers
`list_transformation_layers`	List all transformation layers
`add_transformation_step`	Add transformation steps to layers
`update_transformation_step`	Update transformation step properties
`delete_transformation_step`	Delete transformation steps
`list_transformation_steps`	List steps within a layer
`add_direct_load_step`	Add direct data loading steps
`update_direct_load_step`	Update direct load step properties
`add_bookmark_query`	Add a SPARQL query bookmark to graphmart
`delete_bookmark_query`	Delete a single bookmark from a graphmart
`delete_all_bookmark_queries`	Delete all bookmarks from a graphmart
`refresh_graphmart`	Lightweight refresh of changed layers
`reload_graphmart`	Complete reprocessing of all layers
`get_layer_status`	Comprehensive layer and step error info
`get_step_status`	Specific step debugging

Agent Memory

Tool	Description
`initialize_agent_memory`	Initialize memory for the agent
`write_permanent_memory`	Write to persistent memory
`write_ephemeral_memory`	Write to session-scoped memory
`promote_ephemeral_memory`	Promote ephemeral to permanent memory
`clear_agent_memory`	Clear agent memory
`read_agent_memory`	Read from agent memory

Development

# Install in development mode
pip install -e ".[dev]"

# Run tests
pytest

# Build package
python -m build

Bump Version

Update the version in both:

pyproject.toml → version = "x.y.z"
rmgs_mcp_server/__init__.py → __version__ = "x.y.z"

Publish to PyPI

pip install build twine
python -m build
twine upload dist/*

Project Structure

rmgs-mcp-server/
├── rmgs_mcp_server/           # Main package
│   ├── __init__.py            # Package init with version
│   ├── ags_sparql_agent.py    # MCP server entry point
│   ├── models.py              # Data models
│   ├── server_registry.py     # Multi-server configuration manager
│   ├── sparql_agent_core.py   # Core SPARQL agent logic
│   ├── sparql_query_engine.py # SPARQL query engine
│   ├── ontology_cache.py      # Ontology caching
│   ├── ontology_discovery.py  # Ontology discovery
│   ├── interaction_logger.py  # Logging utilities
│   ├── tools/                 # MCP tool implementations
│   │   ├── base_tool.py       # Base tool class
│   │   ├── system/            # System, server & graphmart tools
│   │   ├── query/             # SPARQL query tools
│   │   ├── discovery/         # Knowledge discovery tools
│   │   ├── ontology/          # Ontology management tools
│   │   ├── graphmart/         # Graphmart construction tools
│   │   └── memory/            # Agent memory tools
│   └── utils/                 # Shared utilities
├── prompts/                   # Agent prompt templates
├── skills/                    # Best practices guides
├── pyproject.toml             # Package metadata & build config
├── LICENSE                    # MIT License
└── README.md                  # This file

License

MIT

This server cannot be installed

license - permissive license

quality - not tested

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

Resources

GitHub Repository

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