AgentTasker MCP Server

AgentTasker MCP Server

<!-- mcp-name: io.github.S3bRR/agent-tasker-mcp -->

AgentTasker is a small, stdio-only MCP server for AI agents that need to run multiple tasks quickly and get structured results back in one call.

It is intentionally narrow:

• two tools: execute and execute_batch
• local stdio transport only
• zero third-party runtime dependencies
• explicit dependency control with depends_on
• compact, model-friendly JSON responses

Repository: https://github.com/S3bRR/agent-tasker-mcp

Why This Exists

Most agent orchestration layers are heavier than they need to be. This project is designed for the common case:

• run a few tasks in parallel
• let one task wait on another when needed
• keep the MCP surface small enough for models to use reliably

There is no queue service, no persistence layer, no background worker system, and no SDK dependency required at runtime.

What It Supports

Task types:

• python_code
• http_request
• discovery_search
• web_scrape
• shell_command
• file_read
• file_write

Public MCP tools:

• execute
• execute_batch

Install

Requirements:

• Python 3.10+
• A local MCP client that can run stdio servers

Recommended: uvx

Run directly from GitHub:

uvx --from git+https://github.com/S3bRR/agent-tasker-mcp.git agent-tasker-mcp-server --workers 8

Once the package is live on PyPI, the command becomes:

uvx agent-tasker-mcp-server --workers 8

pipx

Install directly from GitHub:

pipx install git+https://github.com/S3bRR/agent-tasker-mcp.git

Once the package is live on PyPI, the command becomes:

pipx install agent-tasker-mcp-server

Local clone

git clone https://github.com/S3bRR/agent-tasker-mcp.git
cd agent-tasker-mcp
./setup.sh

setup.sh creates a local .venv, installs this package into it, and prints an absolute MCP config snippet. If python3 -m venv is not available, it falls back to virtualenv when installed.

MCP Client Configuration

GitHub Source

{
  "command": "uvx",
  "args": [
    "--from",
    "git+https://github.com/S3bRR/agent-tasker-mcp.git",
    "agent-tasker-mcp-server",
    "--workers",
    "8"
  ]
}

Installed Package

{
  "command": "agent-tasker-mcp-server",
  "args": ["--workers", "8"]
}

Local checkout

{
  "command": "/absolute/path/to/agent-tasker-mcp/.venv/bin/agent-tasker-mcp-server",
  "args": ["--workers", "8"]
}

Use the exact absolute path printed by ./setup.sh for local checkouts.

Usage

execute

Run one task immediately.

{
  "task_type": "python_code",
  "code": "result = 6 * 7"
}

execute_batch

Run multiple tasks concurrently.

{
  "tasks": [
    {
      "name": "fetch_users",
      "task_type": "http_request",
      "url": "https://api.example.com/users"
    },
    {
      "name": "calc",
      "task_type": "python_code",
      "code": "result = 6 * 7"
    }
  ],
  "output_mode": "compact"
}

depends_on

If one task must wait for another, make it explicit.

{
  "tasks": [
    {
      "name": "write_file",
      "task_type": "file_write",
      "path": "/tmp/example.txt",
      "content": "hello"
    },
    {
      "name": "read_file",
      "task_type": "file_read",
      "path": "/tmp/example.txt",
      "depends_on": ["write_file"]
    }
  ]
}

If an upstream dependency fails, downstream tasks are marked failed and do not run.

Output Shape

output_mode supports:

• compact (default)
• full

The response is ordered to match the input task list, which makes it easier for models to consume without extra reconciliation logic.

Release Process

Releases are tag-driven.

Shortened. The full README is on GitHub.