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Amazon Workspaces Agent Access

AWSOfficialPublished by AWS

  • Skill
  • Tool integration
  • Cloud and DevOps
  • Apache-2.0
  • No issues found

From the author

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.
— the description by AWS

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides domain expertise for integrating AI agents with Amazon WorkSpaces Applications (AppStream 2.0) using the Agent Access MCP server. It covers the technical requirements for establishing secure connections, including SigV4 signing, streaming URL handling, and Active Directory SAML authentication. The skill details how to manage desktop sessions, implement reliable automation through screenshot and action-batching discipline, and utilize MCP tool forwarding. It also provides guidance on troubleshooting common connection errors and configuring stack-level settings for computer input and vision.

What it does

  • Guides the setup of Agent Access MCP connections using SigV4 and streaming URLs.
  • Explains the difference between BLOCKING and POLLING connection modes.
  • Provides best practices for reliable desktop automation, including screenshot budgets and action batching.
  • Details the use of computer-use tools like screenshot, click, type, key, and scroll.
  • Describes how to expose and use forwarded MCP tools within the remote desktop session.
  • Offers troubleshooting steps for common connection and session lifecycle errors.

When to use it

  • When building an AI agent that needs to interact with Windows GUI applications on AppStream 2.0.
  • When debugging connection issues such as dcv session not ready or client_disconnected errors.
  • When optimizing agent performance to reduce token usage and latency in desktop automation.
  • When configuring stack-level security and permissions for agent-driven desktop access.

Advantages

  • Enables secure, agent-driven automation of remote Windows environments.
  • Provides clear patterns for managing session lifecycles and connection states.
  • Reduces token consumption through recommended batching and screenshot strategies.
  • Supports integration with existing MCP servers via tool forwarding.

Usage examples

“How do I configure my agent to wait for the desktop to be ready before sending commands?”
The agent is instructed to use the POLLING connection mode by setting the X-Amzn-AgentAccess-Connect-Mode header to POLLING and repeatedly calling the connection_status tool until the state is CONNECTED.
“My agent is burning too many tokens on screenshots. How can I optimize this?”
The agent is guided to implement a screenshot budget, taking only one screenshot to establish state, performing 3-5 actions, and verifying outcomes by artifact rather than intermediate steps.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/aws/amazon-workspaces-agent-access/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip amazon-workspaces-agent-access.zip -d .agents/skills

Documentation by AWS

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_skill tool: The skill is not installed on the local filesystem. You MUST fetch each reference via retrieve_skill with the file parameter (e.g. file="references/connection-setup.md"), and use the returned content. Do NOT file_read these 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.

Key facts agents get wrong (load the reference before answering in detail)

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.

  • Connect mode. Selected by the X-Amzn-AgentAccess-Connect-Mode HTTP header (value BLOCKING, the default, or POLLING) — sent on the MCP request alongside the streaming-URL/SAML auth. It is NOT a JSON tool argument.

    • ❌ WRONG (common hallucination): calling a connect_to_desktop tool with a connection_mode: "POLLING" parameter, or a session_id/application_id/user_id argument. None of those exist.

    • ✅ RIGHT: set the X-Amzn-AgentAccess-Connect-Mode: POLLING header. Then tools/list initially returns only the connection_status tool; the agent calls connection_status repeatedly until its returned state is CONNECTED, and only then does tools/list return the full desktop tool set (screenshot, left_click, ...). (details: connection-modes.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 set
  • Streaming session (non-domain-joined) is the X-Amzn-AgentAccess-Streaming-Session-Url header. Domain-joined fleets instead pass the SAML assertion + stack ARN via MCP _meta keys aws.agentaccess/workspacesApplicationsSamlAssertion and aws.agentaccess/workspacesApplicationsStackArn. (details: connection-setup.md)

  • Expire-on-delete is the X-Amzn-AgentAccess-Expire-Streaming-Session-On-Delete header (true/false; default false). Expiry happens on the client's explicit HTTP DELETE — mcp-proxy-for-aws sends it automatically on clean close. (details: session-lifecycle.md)

  • Forwarded tools are namespaced by server: forwarded___<server-name>___<tool-name> (e.g. forwarded___filesystem___read_file) — not forwarded___<tool-name>. (details: tool-forwarding.md)

  • COMPUTER_INPUT requires COMPUTER_VISION to also be ENABLED in the stack's AgentAccessConfig. (details: enabling-agent-access.md)

Routing

User needRead
Enable agent access on a stack (AgentAccessConfig: COMPUTER_INPUT/COMPUTER_VISION/FORWARD_MCP_TOOLS, screen resolution/format, prerequisites) — the admin setup step before any agent can connectenabling-agent-access.md
Connect an agent to the MCP server — endpoint, SigV4, streaming URL (non-domain-joined), or Active Directory SAML/Domain Joinconnection-setup.md
Choose BLOCKING vs POLLING; poll connection_status until the desktop is readyconnection-modes.md
The computer-use tool set (mouse, keyboard, screenshot) and their parameterstools-reference.md
Automate reliably — screenshot budget, action batching, trusting UI actions, coordinate planning, dialog recoveryautomation-best-practices.md
Expose your own MCP servers on the fleet as forwarded___<server>___<tool> tools; prefer forwarded tools for file/web taskstool-forwarding.md
Session lifecycle — cleanup, expire-on-delete, idle timeout, one-agent-per-sessionsession-lifecycle.md
Debug an error (exact string → cause → fix): dcv session not ready, client_disconnected, 400/401/403, Unknown tooltroubleshooting.md

Security Considerations

  • The agent acts under the caller's AWS identity. Every MCP request is SigV4-signed with service agentaccess-mcp; the desktop session runs with those credentials. Grant only the specific agentaccess-mcp actions the agent calls (e.g. InvokeMcp, GetScreenshot, LeftClick, TypeText) and scope them with the agentaccess-mcp:StackArn condition key — avoid a blanket agentaccess-mcp:* or Resource: *. Prefer IAM roles over long-lived users. (Full action list + example: connection-setup.md → IAM permissions.)

  • Screenshots can capture sensitive data. COMPUTER_VISION captures whatever is on the desktop — treat screenshots as potentially containing PII or secrets. If screenshot storage is enabled, the S3 bucket must enforce encryption at rest and in transit and least-privilege access: grant the AppStream service principal only what it needs and the connecting agent only s3:PutObject (see enabling-agent-access.md).

  • Enable only the capabilities you need. COMPUTER_INPUT, COMPUTER_VISION, and FORWARD_MCP_TOOLS are independent — do not enable input/forwarding on stacks that only need vision.

  • Tool forwarding executes code on the fleet. Forwarded MCP servers run on the instance under the session context. Install only trusted servers system-wide, gate with FORWARD_MCP_TOOLS, and scope the CallForwardedTool IAM action by agentaccess-mcp:StackArn (see tool-forwarding.md).

  • Keep a human in the loop where warranted. UserControlMode: VIEW_STOP lets an observer watch the live session and stop the agent. Treat agent-driven desktop actions as capable of arbitrary UI operations.

  • Audit with CloudTrail. Agent session events are logged; tool calls are CloudTrail data events and require a trail configured to log them. Create a trail with agentaccess-mcp data events enabled, encrypt it with SSE-KMS, and add CloudWatch alarms for anomalous patterns (e.g. high screenshot volume, unexpected TypeText, repeated auth failures). If screenshot storage is enabled, turn on S3 server access logging for the bucket.

  • Protect federation material. For domain-joined (SAML) fleets, safeguard the IdP signing certificate and the IAM SAML provider/role trust policy, and do not log the SAML assertion. Traffic is HTTPS + SigV4 — never disable TLS verification.

  • Treat typed input as potentially sensitive. type_text can enter secrets (passwords, tokens); these may then appear in screenshots, screenshot-storage S3, and CloudTrail data events. Avoid typing long-lived secrets into the desktop where possible, and restrict who can read those sinks.

  • Refer to the current Agent Access documentation and AWS security best practices for the latest guidance.

Note: Regional endpoints, feature availability, and quotas change. When precision matters, confirm against the current Agent Access MCP server documentation. The references focus on the values and gotchas that are easy to get wrong.

Files

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an MCP server, Needs Python

Security

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Attribution and licence

Published by
AWS
Publisher
AWS (Official)
Source
aws/agent-toolkit-for-aws · skills/specialized-skills/end-user-computing-skills/amazon-workspaces-agent-access · commit 0a218f8
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Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • NOTICE (added by AutoGineer from NOTICE)
  • ATTRIBUTION.md (written by AutoGineer)
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