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Run dependent or parallel agent tasks and return structured results in one call.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "AgentTasker MCP Server" yet — see the docs or source repo.
Use AgentTasker to run three tasks at once: 1) execute Python code to calculate column means from a given CSV, 2) request https://example.com/status to check API status, and 3) run the shell command `df -h` to inspect disk usage. Summarize each task's status, output, and errors, and return everything as structured JSON.
A structured result showing each parallel task's status, stdout, errors, and an overall summary.
Use AgentTasker to first call an API for the latest sales data, then pass the response to a Python task for cleaning and summarization, and finally run a shell command to save the result to a local file. Execute the tasks in dependency order and return each step's input, output, and the final file path.
A step-by-step structured response showing dependencies, intermediate processing results, and the final artifact location.
Use AgentTasker to perform batch checks across multiple targets: request health-check endpoints for five services in parallel, run two shell scripts to collect system information, and execute one Python script to analyze logs. Output a unified result object with success, failure, duration, and recommended actions.
A unified task report listing each check result, duration, error details, and recommended next actions.
Create, manage, and compose AI agents for MCP-compatible clients and tools.
Orchestrate complex AI agent workflows with dependency handling, retries, and progress tracking.
Orchestrate multiple AI agents in real time and monitor tasks and artifacts.
Enable AI coding agents to communicate, share state, and coordinate work in real time.
Schedule and run HTTP jobs with captured responses from your AI agent.
Register, discover, rate, and manage AI agents and services locally.