AI-Powered Feature Management with Harness MCP Server and Claude Code

Managing feature flags can be complex, especially across multiple projects and environments. Teams often need to navigate dashboards, APIs, and documentation to understand which flags exist, their configurations, and where they are deployed. What if you could handle these tasks using simple natural language prompts directly within your AI-powered IDE?

Harness Model Context Protocol (MCP) tools make this possible. By integrating with Claude Code, Windsurf, Cursor, or VS Code, developers and product managers can discover projects, list feature flags, and inspect flag definitions, all without leaving their development environment.

By using one of many AI-powered IDE agents, you can query your feature management data using natural language. They analyze your projects and flags to generate structured outputs that the agent can interpret to accurately answer questions and make recommendations for release planning.

With these agents, non-technical stakeholders can query and understand feature flags without deeper technical expertise. This approach reduces context switching, lowers the learning curve, and enables teams to make faster, data-driven decisions about feature management and rollout.

According to Harness and LeadDev’s survey of 500 engineering leaders in 2024: 

82% of teams that are successful with feature management actively monitor system performance and user behavior at the feature level, and 78% prioritize risk mitigation and optimization when releasing new features.

Harness MCP tools help teams address these priorities by enabling developers and release engineers to audit, compare, and inspect feature flags across projects and environments in real time, aligning with industry best practices for governance, risk mitigation, and operational visibility.

Simplifying Feature Management Workflows

Traditional feature flag management practices can present several challenges:

• Complexity: Understanding flag configurations and environment setups can be time-consuming.
• Context Switching: Teams frequently shift between dashboards, APIs, and documentation.
• Governance and Consistency: Ensuring flags are correctly configured across environments requires manual auditing.

Harness MCP tools address these pain points by providing a conversational interface for interacting with your FME data, democratizing access to feature management insights across teams.

How MCP Tools Work for Harness FME

The FME MCP integration supports several capabilities:

You can also generate quick summaries of flag configurations or compare flag settings across environments directly in Claude Code using natural language prompts.

Some example prompts to get you started include the following:

"List all feature flags in the `checkout-service` project."
"Describe the rollout strategy and targeting rules for `enable_new_checkout`."
"Compare the `enable_checkout_flow` flag between staging and production."
"Show me all active flags in the `payment-service` project."  
“Show me all environments defined for the `checkout-service` project.”
“Identify all flags that are fully rolled out and safe to remove from code.”

These prompts produce actionable insights in Claude Code (or your IDE of choice).

Getting Started

To start using Harness MCP tools for FME, ensure you have access to Claude Code and the Harness platform with FME enabled. Then, interact with the tools via natural language prompts to discover projects, explore flags, and inspect flag configurations.

Installation & Configuration

Harness MCP tools transform feature management into a conversational, AI-assisted workflow, making it easier to audit and manage your feature flags consistently across environments.

Prerequisites

• Go version 1.23 or later
• Claude Code (paid version) or another MCP-compatible AI tool
• Access to the Harness Platform with Feature Management & Experimentation (FME) enabled
• A Harness API key for authentication

Build the MCP Server Binary

1. Clone the Harness MCP Server GitHub repository.
2. Build the binary from source.
3. Copy the binary to a directory accessible by Claude Code.

Configure Claude Code

1. Open your Claude configuration file at `~/claude.json`. If it doesn’t exist already, you can create it.
2. Add the Harness FME MCP server configuration:

{
  ...
  "mcpServers": {
    "harness": {
      "command": "/path/to/harness-mcp-server",
      "args": [
        "stdio",
        "--toolsets=fme"
      ],
      "env": {
        "HARNESS_API_KEY": "your-api-key-here",
        "HARNESS_DEFAULT_ORG_ID": "your-org-id",
        "HARNESS_DEFAULT_PROJECT_ID": "your-project-id",
        "HARNESS_BASE_URL": "https://your-harness-instance.harness.io"
      }
    }
  }
}

3. Save the file and restart Claude Code for the changes to take effect.
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To configure additional MCP-compatible AI tools like Windsurf, Cursor, or VS Code, see the Harness MCP Server documentation, which includes detailed setup instructions for all supported platforms.

Verify Installation

1. Open Claude Code (or the AI tool that you configured).
2. Navigate to the Tools/MCP section.

3. Verify Harness tools are available.

What’s Next

Feature management at scale is a common operational challenge. With Harness MCP tools and AI-powered IDEs, teams can already discover, inspect, and summarize flag configurations conversationally, reducing context switching and speeding up audits.

Looking ahead, this workflow extends itself towards a DevOps-focused approach, where developers and release engineers can prompt tools like Claude Code to identify inconsistencies or misconfigurations in feature flags across environments and take action to address them.

By embedding these capabilities directly into the development workflow, feature management becomes more operational and code-aware, enabling teams to maintain governance and reliability in real time.

For more information about the Harness MCP Server, see the Harness MCP Server documentation and the GitHub repository. If you’re brand new to Harness FME, sign up for a free trial today.