Which integrations are available for this server?

Provides tools for querying and storing insights in a Neo4j knowledge graph using Cypher, enabling structured domain knowledge management.

How do I use SuperAgentic 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., "@SuperAgentic MCP Server Run a simulation with a fault scenario and compute metrics." 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.

SuperAgentic MCP Server

by saxenam06

Overview Schema Related Servers Score Discussions

Python

Local

SuperAgentic

Single-orchestrator architecture for simulation systems with data analytics and knowledge graph capabilities.

Philosophy

Single agent. Dynamic reasoning. Tools execute, you decide.

One orchestrator decides workflow based on query type
Agent writes all SQL, Python, and Cypher queries
Tools only execute - no LLM inside tools
Session persistence with paths passed between tools
Discovery-first: agent learns domain from tool results

Related MCP server: LocalData MCP Server

Architecture

+------------------------------------------------------------------+
|                          SUPERAGENTIC                             |
|                                                                   |
|  +-------------------------+    +------------------------------+  |
|  |     AGENT SERVER        |    |    DIGITAL TWIN SERVER       |  |
|  |                         |    |                              |  |
|  |  +-------------------+  |    |  +------------------------+  |  |
|  |  |   LangGraph       |  |    |  |   MCP SERVER           |  |  |
|  |  |   Workflow        |  |    |  |   (19 tools)           |  |  |
|  |  +-------------------+  |    |  |                        |  |  |
|  |          |              |    |  |  Discovery (5)         |  |  |
|  |  +-------------------+  |    |  |  Data (5)              |  |  |
|  |  |   Orchestrator    |--+--->|  |  Configuration (5)     |  |  |
|  |  |   (ReAct Agent)   |  |    |  |  Simulation (2)        |  |  |
|  |  +-------------------+  |    |  |  Core (2)              |  |  |
|  |                         |    |  +------------------------+  |  |
|  |  core/                  |    |  digitaltwin/                |  |
|  +-------------------------+    +------------------------------+  |
+------------------------------------------------------------------+

Tools

Core (2)

Tool	Purpose
`task_completed(summary)`	Signal completion (required at end)
`ask_user(question)`	Request clarification (use sparingly)

Discovery (5)

Tool	Purpose
`list_options()`	Available controllers, scenarios, faults, profiles
`get_sql_schema(file?)`	List data files or get specific schema
`get_kg_schema()`	Knowledge Graph structure
`list_session_artifacts()`	What's saved in session
`get_data_preview(path)`	Column names and sample data

Data (5)

Tool	Purpose
`get_sql_data(file, sql)`	Query data, returns `saved_path`
`execute_analytics(code, input_path?)`	Run Python calculations, returns `output_path`
`execute_visualization(code, input_path?)`	Create plots, returns `plot_path`
`get_kg_insights(cypher)`	Read from Knowledge Graph
`store_kg_insights(cypher)`	Write to Knowledge Graph

Configuration (5)

Tool	Purpose
`configure(config_json)`	Unified JSON configuration
`configure_controller(type, ...)`	Set controller with params
`configure_scenario(type, ...)`	Set workload scenario
`configure_faults(...)`	Inject actuator faults
`configure_plant(...)`	Set plant physics

Simulation (2)

Tool	Purpose
`run_simulation(steps?)`	Execute simulation
`compute_metrics(profile?)`	Compute performance metrics

Data Flow

get_sql_data ──────> saved_path
                        │
                        v
execute_analytics ──> output_path
                        │
                        v
get_data_preview ──> column names
                        │
                        v
execute_visualization ──> plot_path

Always pass paths between tools. Don't reload data unnecessarily.

Workflow Patterns

Data Analysis

get_sql_schema() → get_sql_data(file, sql) → saved_path
execute_analytics(code, input_path) → output_path
get_data_preview(output_path) → column names
execute_visualization(code, input_path) → plot_path
task_completed(findings)

Simulation

list_options()
configure_controller/scenario/faults
run_simulation()
compute_metrics()
task_completed(results)

Follow-Up Query

list_session_artifacts() → find existing paths
get_data_preview(path) → learn columns
execute_visualization(code, input_path) → create plot
task_completed(summary)

Session Artifacts

All artifacts saved to digitaltwin/sessions/<session_id>/:

sessions/session_YYYYMMDD_HHMMSS/
├── session.json           # Metadata & event log
├── data_snapshots/        # SQL query results (JSON)
├── analysis_code/         # Generated Python code
├── analysis_data/         # Analytics outputs (JSON)
├── visualization_code/    # Generated plot code
└── visualizations/        # Saved plots (PNG/HTML)

Quick Start

# Install dependencies
pip install -r requirements.txt
pip install -r digitaltwin/requirements.txt

# Set API key
export GROQ_API_KEY=your_key

# Run interactive mode
python agent.py

# Single query
python agent.py -q "Analyze temperature patterns"

Environment Variables

# Required
GROQ_API_KEY=your_key

# Optional
GROQ_MODEL=model_name
LLM_PROVIDER=groq  # or "openai"
NEO4J_URI=bolt://localhost:7687
NEO4J_USER=neo4j
NEO4J_PASSWORD=password

Directory Structure

superAgentic/
├── agent.py                # Entry point
├── core/                   # Agent intelligence
│   ├── workflow.py         # LangGraph workflow
│   ├── nodes.py            # ReAct agent node
│   ├── state.py            # State definition
│   ├── context.py          # Event history
│   ├── prompts.py          # System prompt
│   ├── mcp_manager.py      # Tool discovery
│   └── agent_cards.py      # Agent configuration
│
└── digitaltwin/            # Domain implementation
    ├── config/
    │   ├── __init__.py     # Config loader
    │   └── domain_config.yaml
    ├── tools/mcp_server.py # MCP tools
    ├── sessions/           # Session persistence
    ├── data/               # Data files
    ├── simulation/         # Physics engine
    ├── controllers/        # Control algorithms
    ├── runtime/            # Unified runtime
    └── kg/                 # Knowledge Graph

Design Principles

Principle	Rationale
Single Orchestrator	Sequential workflows are optimal for single agent
Agent Writes, Tools Execute	All reasoning in orchestrator, tools just run code
Discovery First	Fetch schema/options before writing queries
Preview Before Viz	Get exact column names to avoid empty plots
Session Persistence	Pass paths between tools, avoid context overflow
Follow-Up Awareness	Check artifacts before redoing work

This server cannot be installed

license - not found

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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