Apache Superset vs Bonnard

Choosing between Apache Superset and Bonnard? Both are database MCP servers, but they lean into different workflows. This page focuses on where each one is actually stronger, not just raw counts.

Choose Apache Superset for

Automating the creation and deployment of BI dashboards for new projects.

Choose Bonnard for

Enabling AI agents to generate accurate, governed business reports from raw warehouse data.

Apache Superset

21by bintocherhttp

Full-featured MCP server for Apache Superset with 128+ tools

Best for Automating the creation and deployment of BI dashboards for new projects.

A comprehensive Model Context Protocol (MCP) server for Apache Superset. Gives AI assistants (Claude, GPT, etc.) full control over your Superset instance — dashboards, charts, datasets, SQL Lab, users, roles, RLS, and more — through 128+ tools.

What it does

  • 128+ MCP tools covering the complete Superset REST API
  • Full security management including users, roles, RLS, and groups
  • Built-in safety validations with confirmation flags and DDL/DML blocking
  • Dashboard native filter management and automatic datasource access synchronization
  • Support for multiple transport options including HTTP, SSE, and stdio

Available tools (5)

dashboard_crudPerform create, read, update, and delete operations on Superset dashboards.
chart_crudManage Superset charts including creation, retrieval, and updates.
sql_lab_queryExecute queries in SQL Lab, format SQL, and export results.
security_managementManage users, roles, groups, and Row Level Security (RLS) policies.
permissions_auditPerform a comprehensive audit of user permissions and access matrices.

Setup requirements

Requires 3 environment variables: SUPERSET_URL, SUPERSET_USERNAME, SUPERSET_PASSWORD. Available via uvx and pip.

View Apache Superset details
vs

Bonnard

19by bonnard-datastdio

Self-hosted semantic layer for AI agents.

Best for Enabling AI agents to generate accurate, governed business reports from raw warehouse data.

Self-hosted semantic layer for AI agents.

Docs · CLI · Discord · Website.

What it does

  • MCP server for AI agents to query semantic layers
  • SQL-based metric definitions with caching and access control
  • Multi-database support including Snowflake, BigQuery, and PostgreSQL
  • Cube Store pre-aggregation cache for fast analytical queries
  • Admin UI for browsing deployed models, views, and measures

Setup requirements

Requires 3 environment variables: ADMIN_TOKEN, CUBEJS_DB_TYPE, CUBEJS_DB_*. Available via NPX and Docker.

View Bonnard details

Biggest differences

CompareApache SupersetBonnard
Best forAutomating the creation and deployment of BI dashboards for new projects.Enabling AI agents to generate accurate, governed business reports from raw warehouse data.
Standout128+ MCP tools covering the complete Superset REST API.MCP server for AI agents to query semantic layers.
Setupuvx or pip, needs 3 env vars, http transport.NPX or Docker, needs 3 env vars, stdio transport.
Transporthttpstdio
Community21 GitHub stars19 GitHub stars

Bottom line

Pick Apache Superset if...

Automating the creation and deployment of BI dashboards for new projects. 128+ MCP tools covering the complete Superset REST API. uvx or pip, needs 3 env vars, http transport.

Pick Bonnard if...

Enabling AI agents to generate accurate, governed business reports from raw warehouse data. MCP server for AI agents to query semantic layers. NPX or Docker, needs 3 env vars, stdio transport.

The real split here is workflow fit, not raw counts. Apache Superset: Automating the creation and deployment of BI dashboards for new projects. Bonnard: Enabling AI agents to generate accurate, governed business reports from raw warehouse data. Public traction is fairly close (21 vs 19 stars).

Keep the comparison logic in memory

Once you pick a server, keep the decision notes, setup rules, and docs in Conare so your agent can apply them again without re-explaining.

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How to set up Apache Superset → How to set up Bonnard →