AgentTasker MCP Server

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

Recommended: uvx

Once the package is live on PyPI:

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

Until then, run directly from GitHub:

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

pipx

Once the package is live on PyPI:

pipx install agent-tasker-mcp-server

Until then:

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

Local clone

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

MCP Client Configuration

Published package

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

GitHub source

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

Local checkout

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

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.

1. update pyproject.toml and server.json to the same version
2. commit and push to main
3. create and push a matching tag such as v1.0.0
4. GitHub Actions runs tests, builds the package, publishes to PyPI through Trusted Publishing, and then publishes server.json to the MCP Registry

The release workflow rejects version drift: the pushed tag, pyproject.toml, and server.json must match exactly.

Limits

Optional environment variables:

• AGENT_TASKER_MAX_TASKS: maximum tasks per execute_batch
• AGENT_TASKER_MAX_PAYLOAD_BYTES: maximum payload size per task
• AGENT_TASKER_MAX_MEMORY_MB: soft process memory guar

... View full README on GitHub