Meet Harness’ Model Context Protocol (MCP) Server: A Smarter Way for AI to Run Your DevOps Workflows

At Harness, we’ve always believed software delivery should be intelligent, efficient, and secure. That’s why AI has been part of our DNA since day one. We first brought AI into software delivery when we introduced Continuous Verification in 2017. That same vision is behind our latest innovation: Harness MCP Server.

This isn’t just another integration tool. It’s a new way for AI agents – whether it’s Claude Desktop, Windsurf, Cursor, or something you’ve built yourself – to securely connect with your Harness workflows. No brittle glue code. No custom APIs. Just smart, consistent connections between your agents and the tools that power your software delivery lifecycle.

What is the Harness MCP Server?

Let’s break it down. The Harness MCP Server runs in your environment and acts as a translator between your AI tools and our platform. It’s a lightweight local gateway that implements the Model Context Protocol (MCP) – an open standard designed to help AI agents securely access external developer services through a consistent, structured interface.

Our customers have repeatedly told us they’re excited to start getting real value from their AI investments, but having secure access to their own data remains a major roadblock. They want to build their own agents, but lack a simple, reliable way to connect them to workflows. Our MCP Server unlocks exactly that. 

“Our customers are building agents, but they don’t need another plugin – they need AI with context. That means access to delivery data from pipelines, environments, and logs. The Harness MCP Server gives them a clean, reliable way to pull that data into their own tools, without fragile integrations. It’s a simple protocol, but it unlocks a lot. And it reflects a broader shift – from AI as a standalone layer to AI as part of the software delivery workflow. We believe that shift is foundational to where DevOps is headed."
—Sanjay Nagraj, SVP Engineering at Harness

Bring the Power of Harness to your AI Workflows

Our MCP Server makes it easy for your AI agents to do more than just observe. They can take action! By exposing a growing set of structured, secure tool sets—including pipelines, repositories, logs, and artifact registries—MCP gives agents consistent access to the same systems your teams already use to build, test, and deploy software. MCP turns Harness into a plug-and-play backend for your AI. Here’s how it works.

✅ One Protocol for Everything

Adapters and glue code slow teams down. But with our MCP server, you don’t need to worry about juggling different adapters or writing custom logic for each Harness service. A single standardized protocol gives agents access to pipelines, pull requests, logs, repositories, artifact registries, and more – all through one consistent interface.

Let’s say a customer success engineer needs to check whether a recent release went out for a specific client. Using their AI agent, the MCP Server will fetch the release data instantly, so they don’t need to waste time pinging their dev team or digging through dashboards.

🔌 Plug-and-Play for Any Agent

We didn’t just build the MCP Server for our own platform – we built it for yours. The same MCP server that powers Harness’ AI agents is available to our customers, making it easy to reuse the same patterns across multiple AI agents and environments. That consistency reduces drift, simplifies maintenance, and cuts down overhead. 

A platform engineer, for example, can build a Slack bot that alerts teams to failed builds and surfaces logs. With MCP, it connects in minutes – no custom APIs, no complex auth flows – just the same server we use internally. 

🔁 Built for Scalability

Innovation never stands still – but your code shouldn’t break just to keep up with it. With our MCP Server, you can add new tool sets and endpoints without changing your agent code.  Simply update your server. And because it's open and forkable, teams can extend functionality to support additional services, internal tools, or custom workflows.

Consider a development team integrating a data source from a product they rely on into VS Code to suggest which pipeline to trigger based on file changes. As their processes evolve, they can keep expanding the agent’s capabilities without ever touching the core agent logic.

🔐 Secure by Design

Security teams need confidence that AI integrations won’t compromise their standards. That’s why our MCP Server is built with enterprise-grade controls from the start. It uses JSON-RPC 2.0 for structured, efficient communication and integrates with Harness’s granular RBAC model so that teams can manage access with precision and prevent unauthorized access. API keys are handled directly in the platform, and no sensitive data is ever sent to the LLM. It’s built to reflect the same security posture customers already trust in Harness.

Take a security team that needs to restrict an agent’s access. With MCP, they can configure the server so the agent is limited to deployment logs – giving support teams the insights they need without opening up the broader system.

🎥 See It in Action

AI is changing how software gets built – but today’s agents are only as helpful as the systems they can safely access. For DevOps and platform teams, this marks a shift from siloed automation to coordinated, AI-driven execution. Instead of building and maintaining custom connectors, teams can now focus on enabling agents to interact with their delivery stack safely, consistently, and at scale.

With the Harness MCP Server, we’re giving developers what they’ve asked for: a more innovative way to connect AI to the software delivery process, without compromising security or speed.

Curious how it all works? Watch our walkthrough video to see the MCP Server in action and learn how AI agents can securely interact with your Harness workflows.

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🧠 Visit the Harness Developer Hub to get started.