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Documentation Index

Fetch the complete documentation index at: https://arivu.mintlify.app/llms.txt

Use this file to discover all available pages before exploring further.

When an MCP client initializes a handshake with Arivu, it registers several modular capabilities directly accessible to the host LLM context layer.

arivu_query

Executes natural language queries dynamically converting intent into safe SQL.

arivu_get_schema

Returns the structural table configurations and column contexts instantly.

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The Tool Registry

Here is the exact suite of tools transmitted during the initialization phase:

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1. Data Interactions

  • arivu_query(question: str): The core pipeline router. Parses a textual question, interfaces with LangGraph, and drops back a heavily verified answer.
  • arivu_get_session_history(session_id: str): Pulls prior conversation context for the designated thread from Arivu memory safely.

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2. Admin & Approvals

If your configuration includes ARIVU_DB_MODE="user", destructive executions will hit an approval queue. MCP clients can view and action these queues natively.
  • arivu_get_pending_approvals(): Lists all operations globally waiting for admin authorization.
  • arivu_approve(session_id: str): Approves the pending SQL mapped to the session, forcing execution successfully.
  • arivu_reject(session_id: str): Hard rejects the destructive operation, reverting pipeline state.

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3. Diagnostics & Cache

  • arivu_refresh_schema(): Triggers a hard re-extraction of backend SQL schema bypassing the set cache TTL. Use this when the underlying database is actively migrating.
  • arivu_test_connection(): Provides an instantaneous health check verifying dialect networking availability.
  • arivu_list_dialects(): Informs the host LLM regarding what dialects the current environment successfully installed (pymysql, psycopg2, etc).