amazon-workspaces-agent-access
Amazon WorkSpaces Applications — Agent Access
Domain expertise for connecting AI agents to remote Windows desktops on Amazon WorkSpaces Applications (AppStream 2.0) via the managed Agent Access MCP server, and for driving those desktops reliably.
How it works: Agent Access is MCP-only — there is no AWS CLI/SDK command that calls the desktop tools. Agents connect to https://agentaccess-mcp.{region}.api.aws/mcp over Streamable HTTP, SigV4-signed with service name agentaccess-mcp, and call MCP tools (screenshot, left_click, type_text, ...) to drive the desktop. The AWS CLI/SDK is used only for setup — appstream create-streaming-url, fleet/stack configuration. mcp-proxy-for-aws handles the SigV4 signing.
Recommended setup: use mcp-proxy-for-aws (Python) as the transport; it signs each request and manages the DELETE lifecycle. Any MCP client that supports Streamable HTTP + SigV4 works. When running the AWS CLI/SDK setup steps (create-streaming-url, stack/fleet configuration), the AWS MCP server is recommended for sandboxed execution and audit logging.
Guardrail — where this skill's own files live (MCP vs local install)
This skill can be loaded two ways, and they resolve the skill's own bundled files from different places. Determine how the skill was loaded before reading a reference:
- Loaded through the AWS MCP
retrieve_skilltool: The skill is not installed on the local filesystem. You MUST fetch each reference viaretrieve_skillwith thefileparameter (e.g.file="references/connection-setup.md"), and use the returned content. Do NOTfile_readthese paths locally — they do not exist on disk. - Installed locally (e.g.
.kiro/skills/amazon-workspaces-agent-access/or~/.claude/skills/amazon-workspaces-agent-access/): Read files from the local skill directory using relative paths.
This distinction applies only to the skill's own packaged files. User data and session artifacts are always read from and written to the user's working directory. Never fetch or write customer data through retrieve_skill.