prometheux-mcp 0.1.3

Model Context Protocol (MCP) server for Prometheux - enabling AI agents to interact with knowledge graphs and reasoning

Prometheux MCP Server

A Model Context Protocol (MCP) client that enables AI agents like Claude to interact with Prometheux knowledge graphs and reasoning capabilities.

---

For Users

What This Does

This package lets you use Claude Desktop to interact with your Prometheux projects:

• List concepts in your projects
• Run concepts to derive new knowledge
• All through natural conversation with Claude

Prerequisites

• Prometheux account with access to a deployed instance
• Claude Desktop installed on your machine
• Your credentials (token, username, organization) from your Prometheux admin

Installation

Using pipx (Recommended)

pipx installs the package in an isolated environment and works reliably with Claude Desktop on all platforms.

macOS:

brew install pipx
pipx ensurepath
pipx install prometheux-mcp

Windows/Linux:

pip install pipx
pipx install prometheux-mcp

Alternative: Using pip (Windows/Linux only)

pip install prometheux-mcp

macOS users: pip may have permission issues with Claude Desktop. Use pipx instead.

Configuration

1. Get your credentials from your Prometheux admin or account settings:

   • Server URL (e.g., https://api.prometheux.ai)
   • Authentication token
   • Username
   • Organization

2. Configure Claude Desktop by editing the config file:

   macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
   Windows: %APPDATA%\Claude\claude_desktop_config.json

   Configuration Example:

   {
     "mcpServers": {
       "prometheux": {
         "command": "prometheux-mcp",
         "args": ["--url", "https://api.prometheux.ai"],
         "env": {
           "PROMETHEUX_TOKEN": "your_token",
           "PROMETHEUX_USERNAME": "your_username",
           "PROMETHEUX_ORGANIZATION": "your_organization"
         }
       }
     }
   }
   

   macOS Users: If you get "Server disconnected" errors, use the full path instead of "prometheux-mcp". Find it with which prometheux-mcp (usually /Users/YOUR_USERNAME/.local/bin/prometheux-mcp or similar).

   Windows Users: If you get "command not found", use the full path like "C:\\Users\\YOUR_USERNAME\\AppData\\Local\\Programs\\Python\\Python310\\Scripts\\prometheux-mcp.exe". Find it with where prometheux-mcp in PowerShell.

   Note: The full path is automatically constructed from your username and organization. No need to include it in the URL!

   Custom URLs: For on-premise deployments or custom URLs, replace https://api.prometheux.ai with your own server URL.

3. Restart Claude Desktop (quit completely with Cmd+Q, then reopen)

Usage

Once configured, just chat with Claude:

"What concepts are available in project customer-analytics?"

"Run the churn_prediction concept in project customer-analytics"

"Show me the high_value_customers from project sales-data with min_value of 1000"

Available Tools

Tool	Description
list_concepts	Lists all concepts in a project
run_concept	Executes a concept to derive new knowledge

Troubleshooting

"command not found" or "Server disconnected" errors:

macOS:

1. Find the full path: which prometheux-mcp
2. Use that full path in your config (usually /Users/YOUR_USERNAME/.local/bin/prometheux-mcp)
3. If still having issues, try pipx: pipx install prometheux-mcp
4. Restart Claude Desktop completely (Cmd+Q, then reopen)

Windows:

1. Find the full path: where prometheux-mcp (in PowerShell or Command Prompt)
2. Use that full path in your config with double backslashes (e.g., C:\\Python310\\Scripts\\prometheux-mcp.exe)
3. Restart Claude Desktop

"Connection refused" error: Check that your Prometheux server URL is correct and accessible. Test with: curl [YOUR_URL]/mcp/info

"Authentication failed" error: Verify your token, username, and organization are correct in the config.

Check logs:

• macOS: ~/Library/Logs/Claude/mcp-server-prometheux.log
• Windows: %APPDATA%\Claude\logs\mcp-server-prometheux.log

---

For Developers

This section is for developers who want to:

• Contribute to this package
• Test locally with a development JarvisPy instance
• Understand the architecture

Architecture

┌─────────────────────────────────────────────────────────────────────────┐
│                           YOUR MACHINE                                   │
│  ┌─────────────────┐         ┌─────────────────┐                        │
│  │  Claude Desktop │         │ prometheux-mcp  │                        │
│  │                 │──stdio──│  (this package) │                        │
│  │   (AI Agent)    │         │                 │                        │
│  └─────────────────┘         └────────┬────────┘                        │
└───────────────────────────────────────┼─────────────────────────────────┘
                                        │ HTTP
                                        ▼
                          ┌─────────────────────────┐
                          │     Prometheux Server   │
                          │       (JarvisPy)        │
                          │                         │
                          │   Cloud or On-Premise   │
                          └─────────────────────────┘

Key points:

• This is NOT a service you run — Claude Desktop starts it automatically
• Communication with Claude Desktop is via stdio (stdin/stdout)
• Communication with Prometheux is via HTTP
• Stateless — each Claude session starts a fresh instance

Local Development Setup

# Clone the repository
git clone https://github.com/prometheuxresearch/px-mcp-server.git
cd px-mcp-server

# Create virtual environment
python -m venv venv
source venv/bin/activate

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

Testing with Local JarvisPy

1. Start JarvisPy in development mode:

   cd /path/to/jarvispy
   source venv/bin/activate
   RUN_MODE=development python run.py
   

2. Install your local package with pipx (required for Claude Desktop on macOS):

   pipx install /path/to/px-mcp-server --force
   

3. Configure Claude Desktop to use localhost:

   {
     "mcpServers": {
       "prometheux": {
         "command": "/Users/YOUR_USERNAME/.local/bin/prometheux-mcp",
         "args": ["--url", "http://localhost:8000", "--debug"]
       }
     }
   }
   

4. Restart Claude Desktop and test

Why pipx for macOS?

Claude Desktop on macOS cannot access virtual environments in protected folders (like ~/Documents) due to security restrictions. pipx installs to ~/.local/ which is accessible.

Running Tests

pytest

Code Quality

ruff check src/    # Linting
mypy src/          # Type checking

Project Structure

src/prometheux_mcp/
├── __init__.py      # Package exports
├── __main__.py      # CLI entry point (Click-based)
├── config.py        # Configuration management
├── client.py        # HTTP client for Prometheux API
├── server.py        # MCP server and tool definitions
└── tools/           # Reserved for future tool modules

Building for PyPI

python -m build
twine upload dist/*

---

Tool Reference

list_concepts

Lists all concepts available in a project.

Parameters:

Parameter	Type	Required	Default	Description
project_id	string	Yes	—	Project identifier
scope	string	No	"user"	"user" or "organization"

Example response:

{
  "concepts": [
    {
      "predicate_name": "customer",
      "fields": {"id": "string", "name": "string"},
      "column_count": 2,
      "is_input": true,
      "row_count": 1000,
      "type": "postgresql",
      "description": "Customer records"
    }
  ],
  "count": 1
}

run_concept

Executes a concept to derive new knowledge through Vadalog reasoning.

Parameters:

Parameter	Type	Required	Default	Description
project_id	string	Yes	—	Project identifier
concept_name	string	Yes	—	Concept to execute
params	object	No	{}	Parameters for reasoning
scope	string	No	"user"	"user" or "organization"
force_rerun	boolean	No	true	Re-execute even if cached
persist_outputs	boolean	No	false	Save results to database

Example response:

{
  "concept_name": "high_value_customers",
  "message": "Concept executed successfully",
  "evaluation_results": {
    "resultSet": {
      "high_value_customers": [["Alice", 5000], ["Bob", 3000]]
    },
    "columnNames": {
      "high_value_customers": ["name", "total_value"]
    }
  },
  "predicates_populated": ["high_value_customers"],
  "total_records": 2
}

---

Access to Prometheux Backend

The Prometheux backend is required to use this MCP client. To request access:

• 📧 Email: [e-mail], [e-mail], or [e-mail]
• 🌐 Website: https://www.prometheux.ai

License

BSD 3-Clause License — see LICENSE file for details.

About Prometheux

Prometheux is an ontology native data engine that processes data anywhere it lives. Define ontologies once and unlock knowledge that spans databases, warehouses, and platforms—built on the Vadalog reasoning engine.

Key capabilities:

• Connect: Query across Snowflake, Databricks, Neo4j, SQL, CSV, and more without ETL or vendor lock-in
• Think: Replace 100+ lines of PySpark/SQL with simple declarative logic. Power graph analytics without GraphDBs
• Explain: Full lineage & traceability with deterministic, repeatable results. Ground AI in structured, explainable context

Exponentially faster and simpler than traditional approaches. Learn more at prometheux.ai.

Support

For issues, questions, or access requests:

• Homepage: https://www.prometheux.ai
• PyPI: https://pypi.org/project/prometheux-mcp/
• Email: [e-mail], [e-mail], or [e-mail]
• Documentation: https://docs.prometheux.ai/mcp
• Issues: GitHub Issues

Related Projects

• Prometheux Chain — Python SDK for Prometheux
• Vadalog Extension — JupyterLab extension for Vadalog
• Vadalog Jupyter Kernel — Jupyter kernel for Vadalog