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Readme

@taskbullet/mcp-server

MCP server for AI agent → human virtual assistant handoff. When your AI agent hits a task it can't complete (phone calls, 2FA-gated logins, physical-world actions, subjective QA), delegate it to a real human VA via the TaskBullet Agent API.

What this is

A Model Context Protocol server that gives any MCP-compatible AI client (Claude Desktop, Claude Code, Cursor, Continue, etc.) native tools to delegate tasks to a real human virtual assistant.

LLMs are brilliant at thinking. They are useless at:

Making phone calls
Logging into 2FA / captcha-gated portals
Subjective quality review ("does this look good?")
Physical-world actions (mail, deliveries, in-person errands)
Long-tail manual data entry

This server bridges that gap. When your AI agent decides a task is beyond its reach, it calls delegate_task and a TaskBullet virtual assistant picks it up.

Tools exposed

Tool	Description
`delegate_task`	Send a new task to a human virtual assistant. Returns a `taskId`.
`list_tasks`	List recently delegated tasks, optionally filtered by status.
`get_task`	Poll the status and result of a specific task by `taskId`.

Install

You need a TaskBullet account and an API key.

Sign up / log in at taskbullet.com
Generate an API key at taskbullet.com/dashboard/settings/api-keys
Wire up the server in your MCP client config (see below).

Configure in Claude Desktop

Edit claude_desktop_config.json (location varies by OS — see Anthropic's docs):

{
  "mcpServers": {
    "taskbullet": {
      "command": "npx",
      "args": ["-y", "@taskbullet/mcp-server"],
      "env": {
        "TASKBULLET_API_KEY": "tb_live_your_key_here"
      }
    }
  }
}

Restart Claude Desktop. You'll see the TaskBullet tools appear in the tools panel.

Configure in Cursor / Continue / other MCP clients

Any client that supports MCP servers via stdio will work. Point it at:

command: npx
args:    -y @taskbullet/mcp-server
env:     TASKBULLET_API_KEY=tb_live_...

Configure in custom agent frameworks

For LangChain, CrewAI, AutoGen, or custom orchestration, you can either:

Use this MCP server directly via an MCP client library, OR
Call the underlying TaskBullet REST API (or the OpenAPI spec) directly.

Example: Claude using delegate_task

You: Find me a flight from SFO to Austin next Tuesday under $300. If you can't book it directly, have someone book it for me.

Claude: I found a Delta flight at 8:15 AM for $278 but it requires phone booking due to a fare class restriction. Let me delegate the booking to your TaskBullet assistant.

[calls delegate_task with title: "Book Delta DL1423 SFO→AUS Tue Jun 3, 8:15 AM, $278. Use my saved AmEx ending 4321. Email confirmation to me."]

Booked task atask_a3f9c2d1. Your assistant will pick this up and you'll get an email when it's confirmed.

Environment variables

Variable	Required	Default	Purpose
`TASKBULLET_API_KEY`	✅	—	Your `tb_live_...` API key
`TASKBULLET_API_BASE`	❌	`https://taskbullet.com/api/v1`	Override for self-hosted or staging
`TASKBULLET_SOURCE_AGENT`	❌	`mcp-client`	Audit label for which agent originated the request

How task completion is delivered back

You have two options:

Poll — your agent calls get_task periodically until status === "completed".
Webhook — pass webhookUrl to delegate_task and TaskBullet POSTs a signed payload when the VA finishes. (See the webhook docs.)

How TaskBullet works under the hood

You pre-purchase a Bucket of Hours (no monthly retainer).
Hours roll over for 90 days so bursty AI workloads don't waste capacity.
Tasks delegated through this MCP server route to your matched, dedicated virtual assistant.
You only spend bucket hours when the VA is actually working on a delegated task — the API itself is free.

Learn more at taskbullet.com.

License

MIT — see LICENSE.

Keywords: mcp server, model context protocol, claude, anthropic, ai agent handoff, human-in-the-loop, virtual assistant, AI delegation, autonomous agent, llm tools, agentic workflow