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AI Agent Integration via MCP

Connect AI agents like Claude to Prometheux using the Model Context Protocol (MCP) — an open standard that enables AI assistants to interact with external tools and data sources.

Prometheux offers two MCP integration options:

  • Local MCP Server (prometheux-mcp): Runs on your machine alongside Claude Desktop
  • Remote MCP Server (px-remote-mcp-server): Cloud-hosted option that works with both Claude Desktop and Claude Web via OAuth authentication

What is MCP?​

The Model Context Protocol allows AI agents to:

  • Discover available tools and resources
  • Execute operations through a standardized interface
  • Access external data sources and APIs

With Prometheux's MCP integration, you can use natural language to interact with your ontologies, list concepts, and execute reasoning — all directly from Claude Desktop or Claude Web.

Which Option Should I Use?​

Best for: Claude Desktop users, development environments, on-premise deployments

Pros:

  • Simple pip install
  • Runs locally (no external dependencies)
  • Full control over credentials
  • Works with any Prometheux instance (cloud or on-premise)

Cons:

  • Requires Claude Desktop app (not available for Claude Web)
  • Needs to be installed on each machine

Use this when:

  • You're using Claude Desktop
  • You want a simple, local installation
  • You're developing or testing locally

Get Started with Local MCP →

Available Tools​

Both MCP servers expose the following tools to AI agents:

list_concepts​

Lists all concepts available in a project.

Parameters:

  • project_id (string, required): The unique identifier of the project
  • scope (string, optional): Search scope - "user" or "organization". Default: "user"

Returns:

  • concepts: Array of concept objects with metadata (name, fields, types, descriptions)
  • count: Total number of concepts found

run_concept​

Executes a concept to derive new knowledge through reasoning.

Parameters:

  • project_id (string, required): The unique identifier of the project
  • concept_name (string, required): Name of the concept to execute
  • params (object, optional): Parameters for the reasoning engine
  • scope (string, optional): Search scope - "user" or "organization". Default: "user"
  • force_rerun (boolean, optional): Re-execute even if results exist. Default: true
  • persist_outputs (boolean, optional): Save derived facts to database. Default: false

Returns:

  • concept_name: The executed concept
  • message: Status message
  • evaluation_results: Reasoning results with derived facts
  • predicates_populated: List of populated predicates
  • total_records: Number of records derived

Example Usage​

Once configured, just chat naturally with Claude. The AI will automatically use the Prometheux MCP tools when relevant.

Example queries:

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

"Run the churn_prediction concept in the customer-analytics project"

"Execute high_value_customers in sales-data with min_value set to 1000"

Claude will automatically:

  • Call the appropriate Prometheux MCP tools
  • Parse the results
  • Present them in a readable format
  • Answer follow-up questions about the data

Learn More​