Tools are defined in the frontmatter to specify which GitHub API calls, browser automation, and AI capabilities are available to your workflow:

Some tools are available by default. All tools declared in imported components are merged into the final workflow.

Allows file editing in the GitHub Actions workspace.

Configure GitHub API operations including toolsets, remote/local modes, and authentication.

tools:
  github:
    toolsets: [repos, issues]

See GitHub Tools Reference for complete configuration options.

Enables shell command execution in the workspace. Defaults to safe commands (echo, ls, pwd, cat, head, tail, grep, wc, sort, uniq, date).

tools:
  bash:                              # Default safe commands
  bash: []                           # Disable all commands
  bash: ["echo", "ls", "git status"] # Specific commands only
  bash: [":*"]                       # All commands (use with caution)

Use wildcards like git:* for command families or :* for unrestricted access.

Enable web content fetching and search capabilities:

tools:
  web-fetch:   # Fetch web content
  web-search:  # Search the web (engine-dependent)

Note: Some engines require third-party Model Context Protocol (MCP) servers for web search. See Using Web Search.

Configure Playwright for browser automation and testing:

tools:
  playwright:
    version: "1.56.1"  # Optional: specify version

See Playwright Reference for complete configuration options, network access, browser support, and example workflows.

Persistent memory storage across workflow runs for trends and historical data.

See Cache Memory Reference for complete configuration options and usage examples.

Repository-specific memory storage for maintaining context across executions.

See Repo Memory Reference for complete configuration options and usage examples.

Provides workflow introspection, log analysis, and debugging tools. Requires actions: read permission:

permissions:
  actions: read
tools:
  agentic-workflows:

See GH-AW as an MCP Server for available operations.

Integrate custom Model Context Protocol servers for third-party services:

mcp-servers:
  slack:
    command: "npx"
    args: ["-y", "@slack/mcp-server"]
    env:
      SLACK_BOT_TOKEN: "${{ secrets.SLACK_BOT_TOKEN }}"
    allowed: ["send_message", "get_channel_history"]

Options: command + args (process-based), container (Docker image), url + headers (HTTP endpoint), registry (MCP registry URI), env (environment variables), allowed (tool restrictions). See MCPs Guide for setup.

The registry field specifies the URI to an MCP server’s installation location in an MCP registry. This is useful for documenting the source of an MCP server and can be used by tooling to discover and install MCP servers:

mcp-servers:
  markitdown:
    registry: "https://api.mcp.github.com/v0/servers/microsoft/markitdown"
    command: "npx"
    args: ["-y", "@microsoft/markitdown"]

When to use:

• Document server source: Include registry to indicate where the MCP server is published
• Registry-aware tooling: Some tools may use the registry URI for discovery and version management
• Both stdio and HTTP servers: Works with both command-based stdio servers and url-based HTTP servers

Examples:

# Stdio server with registry
mcp-servers:
  filesystem:
    registry: "https://api.mcp.github.com/v0/servers/modelcontextprotocol/filesystem"
    command: "npx"
    args: ["-y", "@modelcontextprotocol/server-filesystem"]
# HTTP server with registry
mcp-servers:
  custom-api:
    registry: "https://registry.example.com/servers/custom-api"
    url: "https://api.example.com/mcp"
    headers:
      Authorization: "Bearer ${{ secrets.API_TOKEN }}"

The registry field is informational and does not affect server execution. It complements other configuration fields like command, args, container, or url.

• GitHub Tools - GitHub API operations, toolsets, and modes
• Playwright - Browser automation and testing configuration
• Cache Memory - Persistent memory across workflow runs
• Repo Memory - Repository-specific memory storage
• MCP Scripts - Define custom inline tools with JavaScript or shell scripts
• Frontmatter - All frontmatter configuration options
• Network Permissions - Network access control for AI engines
• MCPs - Complete Model Context Protocol setup and usage
• CLI Commands - CLI commands for workflow management
• Workflow Structure - Directory layout and organization
• Imports - Modularizing workflows with includes