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Minimal stdio MCP server for parallel task execution by AI agents.
Minimal stdio MCP server for parallel task execution by AI agents.
Valid MCP server (1 strong, 1 medium validity signals). No known CVEs in dependencies. Package registry verified. Imported from the Official MCP Registry.
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Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-s3brr-agent-tasker-mcp": {
"args": [
"agent-tasker-mcp-server"
],
"command": "uvx"
}
}
}From the project's GitHub README.
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:
execute and execute_batchdepends_onRepository: https://github.com/S3bRR/agent-tasker-mcp
Most agent orchestration layers are heavier than they need to be. This project is designed for the common case:
There is no queue service, no persistence layer, no background worker system, and no SDK dependency required at runtime.
Task types:
python_codehttp_requestdiscovery_searchweb_scrapeshell_commandfile_readfile_writePublic MCP tools:
executeexecute_batchuvxOnce 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
pipxOnce 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
git clone https://github.com/S3bRR/agent-tasker-mcp.git
cd agent-tasker-mcp
./setup.sh
{
"command": "uvx",
"args": ["agent-tasker-mcp-server", "--workers", "8"]
}
{
"command": "uvx",
"args": [
"--from",
"git+https://github.com/S3bRR/agent-tasker-mcp.git",
"agent-tasker-mcp-server",
"--workers",
"8"
]
}
{
"command": "/absolute/path/to/agent-tasker-mcp/.venv/bin/agent-tasker-mcp-server",
"args": ["--workers", "8"]
}
executeRun one task immediately.
{
"task_type": "python_code",
"code": "result = 6 * 7"
}
execute_batchRun 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_onIf 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_mode supports:
compact (default)fullThe response is ordered to match the input task list, which makes it easier for models to consume without extra reconciliation logic.
Releases are tag-driven.
pyproject.toml and server.json to the same versionmainv1.0.0server.json to the MCP RegistryThe release workflow rejects version drift: the pushed tag, pyproject.toml, and server.json must match exactly.
Optional environment variables:
AGENT_TASKER_MAX_TASKS: maximum tasks per execute_batchAGENT_TASKER_MAX_PAYLOAD_BYTES: maximum payload size per taskAGENT_TASKER_MAX_MEMORY_MB: soft process memory guardThis server is intended for trusted environments.
python_code executes Python codeshell_command executes shell commandsfile_read and file_write operate on the local filesystemDo not expose this server directly to untrusted users.
Create a local environment:
python3 -m venv .venv
source .venv/bin/activate
pip install .
Run the server:
agent-tasker-mcp-server --workers 4
Run tests:
.venv/bin/python -m unittest discover -s tests
This repo includes server.json for MCP Registry publication and a GitHub Actions workflow that publishes both the PyPI package and MCP metadata from a version tag.
MIT
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