Best Databricks MCP MCP Server Alternatives 2026
Updated June 202610 alternatives to Databricks MCP for your AI workflow. Compare features, pricing, and compatibility.
Databricks MCP
Open SourceManage Databricks lakehouse workloads — run SQL queries via Databricks SQL warehouses, submit and monitor Spark jobs and workflows, query Delta Lake table metadata, manage cluster lifecycle and autoscaling, retrieve job run history and output, access Unity Catalog and data governance APIs, manage notebooks and repos, and query ML experiment and model registry data via the Databricks REST API.
This MCP server is free and open-source. Check the GitHub repository for details.
Top Databricks MCP Alternatives
Integrate DeepSeek API for cost-effective reasoning and code generation — access DeepSeek-V3 and DeepSeek-R1 models for complex problem solving, mathematical reasoning, and code synthesis.
This MCP server is free and open-source. Check the GitHub repository for details.
Access Alibaba Cloud Qwen LLM family via API — long-context processing with Qwen-Long, multimodal with Qwen-VL, and code generation with Qwen-Coder for multilingual AI workflows.
This MCP server is free and open-source. Check the GitHub repository for details.
Connect to Moonshot AI Kimi API for long-context document analysis up to 200K tokens, Chinese-English bilingual processing, web search-augmented generation, and multimodal understanding.
This MCP server is free and open-source. Check the GitHub repository for details.
a Python MCP server for Jenkins (lanbaoshen/mcp-jenkins, published to PyPI as mcp-jenkins) and the alternative to the official Jenkins MCP plugin: where the plugin has to be installed into the controller from the update centre, this one runs beside Jenkins and talks to it over the ordinary REST API, so you can point it at a Jenkins you do not administer. Install it with uvx mcp-jenkins, pip install mcp-jenkins, or the ghcr.io/lanbaoshen/mcp-jenkins container. Credentials go in as --jenkins-url, --jenkins-username and --jenkins-password (an API token works in place of the password), and on the HTTP transport the same three can be supplied per-connection as the x-jenkins-url, x-jenkins-username and x-jenkins-password headers, which is what lets one running instance serve several Jenkins controllers. Three transports are available via --transport: stdio (the default), sse, and streamable-http, which listens on 0.0.0.0:9887 unless --host and --port say otherwise. The tool set is wider than most Jenkins integrations. Jobs and pipelines: get_item, get_item_config, get_item_parameters, get_all_items, query_items for pattern search, and build_item to trigger. Builds: get_build, get_build_console_output, get_build_parameters, get_build_test_report, get_build_scripts, get_running_builds and stop_build, plus get_all_build_artifacts, get_build_artifact and get_build_artifact_url for pulling artifacts out of a finished run. Queue: get_all_queue_items, get_queue_item, cancel_queue_item. Agents: get_all_nodes, get_node, get_node_config. The plugin tools are the part with no equivalent elsewhere — get_all_plugins, get_plugin, get_plugins_with_updates, get_plugins_with_backup, get_plugin_dependency_graph (Graphviz output), and get_plugins_with_problems, which reports missing dependencies and version mismatches and turns a plugin upgrade audit into one question. Two flags matter for safety. run_groovy_script executes arbitrary Groovy on the controller, which is full remote code execution against your CI; --read-only disables it along with every other mutating tool and is the right default for an agent that only needs to read build state. --jenkins-timeout defaults to 5 seconds, which is often too short for a large get_all_items on a busy controller.
This MCP server is free and open-source. Check the GitHub repository for details.
Connect to JetBrains TeamCity CI/CD via REST API — trigger build configurations, monitor build queues, inspect test results, manage agents, and query build artifacts for developer workflow automation.
This MCP server is free and open-source. Check the GitHub repository for details.
Manage Atlassian Bamboo CI/CD pipelines via API — run plans, inspect build results, manage deployment environments, track releases, and integrate with Jira and Bitbucket for full DevOps workflows.
This MCP server is free and open-source. Check the GitHub repository for details.
Control Drone CI/CD pipelines via API — trigger and monitor builds, inspect pipeline steps, manage repositories, read logs, and automate container-native continuous integration workflows.
This MCP server is free and open-source. Check the GitHub repository for details.
Manage infrastructure-as-code at scale with Spacelift API — trigger Terraform/OpenTofu/Pulumi stacks, manage policies, monitor runs, handle drift detection, and orchestrate multi-cloud deployments.
This MCP server is free and open-source. Check the GitHub repository for details.
Automate infrastructure provisioning with Ansible AWX/Tower REST API — launch job templates, manage inventories, run ad-hoc commands, query task results, and orchestrate playbook execution.
This MCP server is free and open-source. Check the GitHub repository for details.
Manage and monitor Apache Airflow DAGs, trigger pipeline runs, inspect task logs, pause/unpause workflows, and query DAG run history — full workflow orchestration control from your AI assistant.
This MCP server is free and open-source. Check the GitHub repository for details.
Frequently Asked Questions
What are the best alternatives to Databricks MCP MCP Server?
The top alternatives to Databricks MCP MCP Server in 2026 include DeepSeek MCP, Qwen (Alibaba Cloud) MCP, Moonshot AI (Kimi) MCP, MCP Jenkins, TeamCity MCP. Each offers similar functionality in the data category with different features, pricing, and compatibility.
Is there a free alternative to Databricks MCP MCP Server?
Yes, free alternatives to Databricks MCP include DeepSeek MCP, Qwen (Alibaba Cloud) MCP, Moonshot AI (Kimi) MCP. These offer free tiers or are completely open-source.
How do I choose between Databricks MCP and its alternatives?
When choosing between Databricks MCP and alternatives, consider: (1) Pricing — compare free tiers and paid plans, (2) Features — what specific capabilities you need, (3) Compatibility — which AI assistants (Claude, Cursor, VS Code) are supported, (4) Installation — npm, pip, docker, or other install methods.
Can I use multiple MCP servers at the same time?
Yes! MCP (Model Context Protocol) supports running multiple servers simultaneously. You can use Databricks MCP alongside other MCP servers to extend your AI assistant's capabilities across different services and tools.