Can the AI query databases connected to Superset, such as Presto or Redshift?

Yes. The `execute_sql_query` tool runs queries through Superset's SQL Lab API, which routes them to whichever database engine you have configured — Presto, Redshift, PostgreSQL, and others.

Are dashboard access permissions enforced when using the MCP server?

Yes. All requests are authenticated with your `SUPERSET_ACCESS_TOKEN`, so only the dashboards, charts, and datasets your token has access to will be visible to the AI.

Can the AI run write queries (INSERT, UPDATE, DELETE) via SQL Lab?

It depends on your Superset database connection settings. By default, SQL Lab connections are read-only. Write access must be explicitly enabled per database in the Superset admin panel.

Apache Superset MCP Server for LlamaIndex 7 tools — connect in under 2 minutes

Name: Apache Superset
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 7 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Apache Superset as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Apache Superset. "
            "You have 7 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Apache Superset?"
    )
    print(response)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Apache Superset MCP Server

Empower your conversational AI with deep Business Intelligence access by integrating the Apache Superset MCP connector. Seamlessly navigating complex data ecosystems natively from your LLM text-interface, your agent can comprehensively index your analytical infrastructure—spanning from high-level operational dashboards down to specific raw database connections. Instantly run ad-hoc data investigations utilizing internal SQL Lab queries, retrieve explicit graph metadata, and dynamically aggregate critical business insights without abandoning your development environment.

LlamaIndex agents combine Apache Superset tool responses with indexed documents for comprehensive, grounded answers. Connect 7 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

What you can do

Discover Analytics Surfaces — Audit your entire BI portal intuitively by executing list_dashboards and retrieve exact metric configurations invoking get_dashboard_details.
Graph & Dataset Inspection — Inventory active metrics logic seamlessly via list_charts (or specify via get_chart_details) and map semantic layers dynamically performing list_datasets.
Uncover Data Architectures — Examine exact backend storage clusters accurately parsing data availability via list_databases natively.
Direct SQL Processing — Interface with your central storage matrices seamlessly by generating raw extractions securely via execute_sql_query targeting specific analytic connections.

The Apache Superset MCP Server exposes 7 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Apache Superset to LlamaIndex via MCP

Follow these steps to integrate the Apache Superset MCP Server with LlamaIndex.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 7 tools from Apache Superset

Why Use LlamaIndex with the Apache Superset MCP Server

LlamaIndex provides unique advantages when paired with Apache Superset through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Apache Superset tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Apache Superset tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Apache Superset, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Apache Superset tools were called, what data was returned, and how it influenced the final answer

Apache Superset + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Apache Superset MCP Server delivers measurable value.

Hybrid search: combine Apache Superset real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Apache Superset to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Apache Superset for fresh data

Analytical workflows: chain Apache Superset queries with LlamaIndex's data connectors to build multi-source analytical reports

Apache Superset MCP Tools for LlamaIndex (7)

These 7 tools become available when you connect Apache Superset to LlamaIndex via MCP:

execute_sql_query

Provide a database ID and the SQL statement. Executes a SQL query via SQL Lab

get_chart_details

Retrieves details for a specific chart

get_dashboard_details

Retrieves details for a specific dashboard

list_charts

Lists all charts (slices) in Superset

list_dashboards

Lists all available dashboards in Apache Superset

list_databases

Lists connected data source connections

list_datasets

Lists all datasets available for analysis

Example Prompts for Apache Superset in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Apache Superset immediately.

"List all my Superset dashboards and tell me which one was updated most recently."

"Check our database connections to see if our Postgres 'SalesDB' is active."

"Run a SQL Lab query to show the top 5 product categories by revenue in SalesDB."

Troubleshooting Apache Superset MCP Server with LlamaIndex

Common issues when connecting Apache Superset to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Apache Superset + LlamaIndex FAQ

Common questions about integrating Apache Superset MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Apache Superset tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.