"Running queries in Apache Superset" matching MCP servers:

Connects LLM clients to Apache Superset for querying charts, dashboards, datasets, databases, and executing SQL.

Enables AI agents to manage Apache Superset BI metadata (charts, dashboards, datasets, and queries) via MCP, with a read-only default and optional governed write access.

Full-featured MCP server for Apache Superset — 135+ tools for dashboards, charts, datasets, SQL Lab, security (users, roles, RLS, groups), audit, and more. Built-in safety validations.

An MCP server that provides AI assistants with full access to Apache Superset instances, enabling interaction with dashboards, charts, datasets, databases, and SQL execution capabilities.

Enables natural language querying and management of Apache Superset dashboard metadata, including user activity, data lineage, and change tracking.

A Model Context Protocol (MCP) server for managing Apache Superset datasets, metrics, and SQL queries.

Enables AI to create and manage Apache Superset dashboards, including adding charts and native filters from datasets.

MCP server enabling AI agents to programmatically connect to and control Apache Superset instances, allowing users to manage dashboards, charts, databases, datasets, and run SQL queries through natural language interactions.

A Model Context Protocol server that enables large language models to interact with Apache Superset databases through REST API, supporting database queries, table lookups, field information retrieval, and SQL execution.

Enables MCP clients such as Claude to browse and edit Apache Superset dashboards, charts, and datasets, run and manage SQL Lab queries, and administer databases and tags. Authenticates via session cookie plus CSRF (with JWT fallback) and returns fully paginated, compact listings so large instances stay within the model's context.

A comprehensive MCP server for Apache Superset. Gives AI assistants full control over your Superset instance — dashboards, charts, datasets, SQL Lab, users, roles, RLS, and more — through 137 tools.

An MCP server for Apache Superset that enables AI models to manage dashboards, create charts with auto-detection, and execute SQL queries programmatically. It utilizes stateless HTTP transport to ensure compatibility with integration tools like Open WebUI.

Provides AI agents with real-time ground truth about what's actually running on the machine, including live agent sessions, listening TCP ports, loaded daemons, and system stats. It reads directly from the OS via fixed commands, ensuring agents never rely on stale docs or transcripts.

Enables ChatGPT or another MCP client to query a self-hosted, email-restricted Garmin Connect running archive — latest and listed runs, lap and chart details, run comparisons, trends, recovery and training context — all served read-only from a Cloudflare Worker cache with one explicit, rate-limited manual refresh. Garmin credentials are never stored, and no tool can modify the Garmin account.

Connects AI clients to a personal Intervals.icu account to retrieve recent running activities as clean, normalized JSON. It supports read-only lookups filtered by number of runs and how many days back to search.

Enables conversational analysis of n8n workflows and Make scenarios for silent-failure risks, with tools to explain findings, compare workflow versions, and list rules.

Enables querying and managing Apache Druid datasources through natural language, including SQL queries, datasource exploration, and cluster connectivity testing.

Connects LLM agents to Apache Atlas for searching, tracing lineage, and managing metadata. Read-only by default, with optional write mode for creating entities and classifications.

Provides comprehensive running performance calculations including VDOT, training paces, race time predictions, velocity markers, and heart rate zones using Jack Daniels, Greg McMillan, and Riegel methodologies.

Enables LLM agents to interact with Apache Zeppelin notebooks via the Zeppelin REST API, supporting operations like listing, searching, editing, and running notebooks.

Gives Claude and ChatGPT read-only access to locally stored Garmin running and health data, so users can ask about recovery status, individual runs, best efforts, sleep and long-term trends, pull computed weekly reports, or run read-only SQL for anything else. It also triggers an on-demand sync so the answers reflect the latest data.

MCP server for amazon.in — cheapest in-stock, best-value picks, and price history links.

Enables inspection and management of Apache Airflow DAGs, runs, and logs across multiple instances. It provides tools for monitoring workflows and performing gated write operations like triggering DAGs or clearing task instances.

Local stdio MCP server for reviewing Apache Superset dashboards, retrieving chart data, executing SQL through SQL Lab, and creating or updating saved queries.

Enables users to describe their LLM fine-tuning job once and get the cheapest, fastest, and most balanced GPU options across a dozen cloud providers in seconds.

Provides a standardized way for MCP clients to interact with Apache Airflow's REST API, supporting operations like DAG management and monitoring Airflow system health.

A proof of concept MCP server for Apache OFBiz

Enables an Alexa+ host to place and manage customer-care calls by building a case, navigating phone menus, waiting on hold, explaining the issue, guarding sensitive disclosures, handing off identity checks, recording ticket numbers and promises, and scheduling follow-ups.

An MCP server that wraps the Apache Airflow REST API, enabling clients to manage DAGs, monitor task instances, and handle workflows through a standardized interface. It provides comprehensive access to Airflow features including DAG runs, variables, connections, and XComs.

MCP server that lets AI assistants and LLMs interact with Apache Flink clusters through natural language: cluster/job monitoring, metrics, exception tracking, TaskManager resources, and multi-cluster support via the Flink REST API.