AgentTasker MCP Server
Minimal stdio MCP server for parallel task execution by AI agents.
Open source Open in the app JSON README (API)
About
Minimal stdio MCP server for parallel task execution by AI agents.
Details
- Kind
- MCP servers
- Topic
- No topic detected
- Publisher
- s3brr
- Origin
- official
- Category
- ferramentas
- Transport
- local
- Version
- 1.0.1
- Last push
- 2026-04-26T06:27:29Z
- Repository state
- ativo
- Language
- Python
- License
- MIT
- Added
- 2026-08-29 03:02:13
- Updated
- 2026-08-29 03:02:13
- Origin id
io.github.S3bRR/agent-tasker-mcp
README
# 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:
```bash
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:
```bash
uvx agent-tasker-mcp-server --workers 8
```
### `pipx`
Install directly from GitHub:
```bash
pipx install git+https://github.com/S3bRR/agent-tasker-mcp.git
```
Once the package is live on PyPI, the command becomes:
```bash
pipx install agent-tasker-mcp-server
```
### Local clone
```bash
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
```json
{
"command": "uvx",
"args": [
"--from",
"git+https://github.com/S3bRR/agent-tasker-mcp.git",
"agent-tasker-mcp-server",
"--workers",
"8"
]
}
```
### Installed Package
```json
{
"command": "agent-tasker-mcp-server",
"args": ["--workers", "8"]
}
```
### Local checkout
```json
{
"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.
```json
{
"task_type": "python_code",
"code": "result = 6 * 7"
}
```
### `execute_batch`
Run multiple tasks concurrently.
```json
{
"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.
```json
{
"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 guard
## Security Notes
This server is intended for trusted environments.
- `python_code` executes Python code
- `shell_command` executes shell commands
- `file_read` and `file_write` operate on the local filesystem
Do not expose this server directly to untrusted users.
## Development
Create a local environment:
```bash
./setup.sh
source .venv/bin/activate
```
Run the server:
```bash
agent-tasker-mcp-server --workers 4
```
Run tests:
```bash
.venv/bin/python -m unittest discover -s tests
```
## Packaging
This repo includes [server.json](./server.json) for MCP Registry publication and a GitHub Actions workflow that publishes both the PyPI package and MCP metadata from a version tag.
## License
MIT