analyzing-release-read…
Trigger a pre-merge release readiness review on a GitHub PR, GitLab MR, or local branch. Use when the user wants to analyze code changes for risk, correctness,…
Connects AI agents to remote Windows desktop applications on Amazon WorkSpaces Applications (AppStream 2.0) through the managed Agent Access MCP server, and guides reliable desktop automation. Covers connecting an agent to the MCP endpoint (SigV4, streaming URL, and Active
$ npx -y skills add aws/agent-toolkit-for-aws --skill amazon-workspaces-agent-access --agent claude-codeHow it fires
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/amazon-workspaces-agent-accessContext preview
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Connects AI agents to remote Windows desktop applications on Amazon WorkSpaces Applications (AppStream 2.0) through the managed Agent Access MCP server, and guides reliable desktop automation. Covers connecting an agent to the MCP endpoint (SigV4, streaming URL, and Active
name: amazon-workspaces-agent-access description: Connects AI agents to remote Windows desktop applications on Amazon WorkSpaces Applications (AppStream 2.0) through the managed Agent Access MCP server, and guides reliable desktop automation. Covers connecting an agent to the MCP endpoint (SigV4, streaming URL, and Active Directory SAML/Domain Join), BLOCKING vs POLLING connect modes, the computer-use tools (screenshot, click, type, key, scroll), screenshot-budget and action-batching discipline, MCP tool forwarding (forwarded___ tools), session lifecycle and expire-on-delete, and troubleshooting connection errors. Use when building or debugging an agent that drives a remote Windows desktop or GUI application via WorkSpaces Applications / AppStream — including "dcv session not ready", "client_disconnected", 400 signing-region, POLLING/connection_status, SAML assertion, or forwarded tool questions. Not for Amazon WorkSpaces Personal/Core virtual desktops or general AppStream fleet administration unrelated to agent access. version: 1
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.
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:
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`.
These are HTTP headers / metadata on the MCP connection — **not** tool parameters, and there is no `connect_to_desktop` tool. Do not invent tools or parameters; the desktop tools are exactly those in tools-reference.md.
# Correct POLLING usage — the mode is an HTTP header on the MCP connection:
async with aws_iam_streamablehttp_client(
endpoint="https://agentaccess-mcp.us-east-1.api.aws/mcp", # use YOUR fleet's region
aws_service="agentaccess-mcp", aws_region="us-east-1", # region must match the fleet (else 400)
headers={
"X-Amzn-AgentAccess-Streaming-Session-Url": streaming_url,
"X-Amzn-AgentAccess-Connect-Mode": "POLLING", # header, not a tool arg
},
) as (read, write, _):
async with ClientSession(read, write) as session:
await session.initialize()
# tools/list now returns ONLY connection_status until the desktop is up:
while json.loads((await session.call_tool("connection_status", {})).content[0].text)["state"] != "CONNECTED":
await asyncio.sleep(2)
tools = await session.list_tools() # now the full desktop tool setHelp AI coding agents build, deploy, and manage applications on AWS. The Agent Toolkit for AWS gives AI coding agents the tools, knowledge, and guardrails they need to work with AWS services.
Repo: aws/agent-toolkit-for-aws
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