Best practices & adding tools via MCP

Overview

Coder templates should be pre-equipped with the tools and dependencies needed for development. With AI Agents, this is no exception.

Prerequisites

• A Coder deployment with v2.21 or later
• A template configured for AI agents

Best Practices

• Since agents are still early, it is best to use the most capable ML models you have access to in order to evaluate their performance.
• Set a system prompt with the AI_SYSTEM_PROMPT environment in your template
• Within your repositories, write a .cursorrules, CLAUDE.md or similar file to guide the agent's behavior.
• To read issue descriptions or pull request comments, install the proper CLI (e.g. gh) in your image/template.
• Ensure your template is truly pre-configured for development without manual intervention (e.g. repos are cloned, dependencies are built, secrets are added/mocked, etc.)

  Note: External authentication can be helpful to authenticate with third-party services such as GitHub or JFrog.

• Give your agent the proper tools via MCP to interact with your codebase and related services.
• Read our recommendations on securing agents to avoid surprises.

Adding Tools via MCP

Model Context Protocol (MCP) is an emerging standard for adding tools to your agents.

Follow the documentation for your agent to learn how to configure MCP servers. See modelcontextprotocol/servers to browse open source MCP servers.

Our Favorite MCP Servers

In internal testing, we have seen significant improvements in agent performance when these tools are added via MCP.

• Playwright: Instruct your agent to open a browser, and check its work by viewing output and taking screenshots.
• desktop-commander: Instruct your agent to run long-running tasks (e.g. npm run dev) in the background instead of blocking the main thread.

Next Steps