Spinnaker MCP, developed by Geiserx, is an MCP server designed to integrate AI models with the Spinnaker continuous delivery platform. This free tool bridges any Spinnaker instance through its Gate API, enabling intelligent automation for deployment and operational tasks. Primarily written in Go, despite some TypeScript and JavaScript components, its core server is distributed as a pre-built Go binary via npm or a Docker image. It serves as a vital link for AI agents seeking to interact with Spinnaker workflows.

What does Spinnaker MCP connect to?

Spinnaker MCP functions as an MCP server, establishing a connection to Spinnaker, an open-source, multi-cloud continuous delivery platform. It specifically bridges any Spinnaker instance by utilizing its Gate API. This integration allows AI models to interact directly with Spinnaker's capabilities, enabling automated control and data retrieval from the delivery platform. The server supports interaction with various Spinnaker components through its exposed tools.

Core features and tools

Spinnaker MCP exposes several callable tools for interacting with Spinnaker. These include managing Applications (list_applications, get_application), Pipelines (list_pipelines, trigger_pipeline, save_pipeline, delete_pipeline), and Executions (list_executions, cancel_execution, pause_execution, resume_execution). It also provides tools for Strategies (list_strategies, save_strategy), Infrastructure (list_server_groups, list_load_balancers, get_cluster, find_images), and Tasks (get_task). These tools allow AI models to query and manipulate Spinnaker's delivery processes.

Who is it for?

Spinnaker MCP is intended for AI models, large language models, and agents that need to interact with continuous delivery processes. It also serves product engineers and developers creating AI applications and agentic workflows that involve Spinnaker. The tool allows these groups to automate and enhance their deployment and operational activities. This is not the right fit if your workflow does not involve integrating AI models with Spinnaker.

How to get started

The server authenticates using Bearer tokens, Basic authentication, or x509 client certificates. Setup involves configuring environment variables such as GATE_TOKEN, GATE_USER, and GATE_PASS, and it supports stdio and Streamable HTTP transport protocols.

Use cases

• Intelligent Deployment Decisions: AI models analyze application and pipeline states to determine optimal deployment times and environments.
• Proactive Issue Detection and Autonomous Remediation: AI detects potential issues like vulnerabilities or slow deployments and initiates corrective actions.
• Automated Root Cause Analysis and Recovery: AI diagnoses problems and takes corrective measures, such as rolling back to a previous version.

Spinnaker MCP offers a direct pathway for AI models to interact with Spinnaker, enabling advanced automation of delivery pipelines. Its support for various authentication methods and transport protocols makes it adaptable for different AI clients and agentic systems. The tool provides essential capabilities for managing applications, pipelines, and infrastructure within Spinnaker.