Connect AI agents 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 three MCP integration options:
  • Local MCP Server (prometheux-mcp): Runs on your machine — works with Claude Desktop, Claude Code, Cursor, and any stdio-capable client. Recommended for coding agents.
  • Remote MCP Server (px-remote-mcp-server): Cloud-hosted option that works with any MCP-compatible client via OAuth authentication
  • Built-in MCP Server (Snowflake Native App only): Automatically registered when the app starts — Cortex Agents and Snowflake Intelligence can discover Prometheux tools with no external setup. See the Native App guide for details.

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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 your preferred AI agent.

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Which Option Should I Use?

Best for: Coding agents (Claude Code, Cursor), Claude Desktop, development environments, on-premise deploymentsPros:
  • Simple pip install
  • Runs locally (no external dependencies)
  • Full control over credentials — no OAuth flow required
  • Works with any Prometheux instance (cloud or on-premise)
  • Compatible with Claude Desktop, Claude Code, Cursor, and any stdio-capable client
Cons:
  • Needs to be installed on each machine
Use this when:
  • You’re using a coding agent (Claude Code, Cursor)
  • You’re using Claude Desktop
  • You want a simple, local installation
  • You’re developing or testing locally
Get Started with Local MCP →

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Supported MCP Clients

We provide step-by-step connection guides for the following clients:

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Chat & Enterprise Clients (Remote Server)

Don’t see your client? For stdio-capable clients (IDEs, terminal agents), use the Local MCP Server. For browser-based clients, the Remote MCP Server exposes a standard OAuth endpoint.

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Available Tools

Both MCP servers dynamically expose the following tools to AI agents. The tool catalog is served by the Prometheux backend, so new capabilities are available automatically as they are added to the platform. Required parameters are shown in bold; the rest are optional.
Most clients fetch the tool catalog once, when the connection opens, and cache it for the rest of the session. If a tool starts rejecting a parameter that this page documents, your client is probably holding an older copy of the schema — reconnect to the server to pick up the current one.

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Ontologies

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Data sources

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Concepts — read and run

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Concepts — authoring

Writing a conceptconcept_type is one of logic (Vadalog), sql, cypher, python, context, or llm, and defaults to logic. The body always goes in definition — a Vadalog program, a query, a Python body, or an LLM prompt template, depending on the type.context concepts are the exception: they have no body at all, and are configured entirely through concept_config. llm concepts use definition for the prompt and concept_config for the output columns and model settings. See Context and LLM concepts.create_concept also guards against duplication: if a similar concept already exists in another of your ontologies, the save is rejected with similar_existing_concepts, and you re-issue the call with confirm_new: true to proceed.

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Snapshots

restore_snapshot takes an automatic safety snapshot before it overwrites anything.

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Schema

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Apps

Call get_app_schema before save_app — it returns the authoritative JSON Schema for the app definition, along with the authoring guide.

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Context Layer

Here scope is the note’s own scope — global or project — rather than the workspace scope used elsewhere. kind is preference or fact.

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Skills

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Compute machines

Note the two different identifiers: machine_id is a catalogue id, used only by start_machine, while user_machine_id identifies a machine already on your list.

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Scheduled runs

trigger_type is either cron, whose config looks like {"cron_expression": "0 6 * * *"}, or data_change, whose config looks like {"check_interval_minutes": 30}.

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Vadalog and company knowledge

Parameter defaultsTools that expose a scope parameter accept "user" or "organization" and default to "user"; tools that omit it operate on your personal workspace. On run_concept, both force_rerun and persist_outputs default to true.On update_concept, omitting description, concept_config, or group leaves that field unchanged — which is different from passing an empty value, since an empty concept_config clears it and an empty group moves the concept back to the root.
Long-running toolsrun_concept, ingest_document_as_context, and extract_concepts_from_document advertise themselves as long-running through the prometheux/long_running hint in tools/list, so clients can keep the connection alive and surface progress instead of timing out.

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Example Usage

Once configured, just chat naturally with your AI agent. The agent will automatically use the Prometheux MCP tools when relevant. Example queries:
“What ontologies do I have?”
“What concepts are available in my customer-analytics ontology?”
“Run the churn_prediction concept in the customer-analytics ontology”
“Write a concept that finds all transitive suppliers for a company”
“Summarise each supplier’s risk notes into a table of company, risk level, and reason”
“Create an app showing the results of churn_prediction”
“What does the Vadalog documentation say about recursive rules?”
The AI agent will automatically:
  • Call the appropriate Prometheux MCP tools
  • Parse the results
  • Present them in a readable format
  • Answer follow-up questions about the data

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Learn More