Agent Task Decomposition Engine

Agent Task Decomposition Engine MCP Connector for Claude

A+

Break complex goals into structured subtasks with deterministic complexity scoring.

3 tools Official Updated Oct 1, 2026 Official Vinkius Partner

This MCP server provides precise tools for breaking down high-level objectives into actionable hierarchies. Use decompose_task to generate a structured tree of subtasks, analyze_complexity to calculate metrics like parallelism potential and optimal subtask counts, and validate_dependencies to detect circular references. It is designed to help AI agents plan effectively by quantifying task difficulty and ensuring logical task flow.

decompositioncomplexitytask-planningagent-orchestrationlogic

3 tools expose this connector's capabilities to your AI agent.

analyze_complexity

Calculate the structural metrics of a previously decomposed task tree

decompose_task

Break a high-level task into a hierarchical structure of subtasks and leaf actions

validate_dependencies

Ensure the task decomposition does not contain logical loops

See how to talk to your AI agent using Agent Task Decomposition Engine.

Decompose the task: 'Build a complete marketing campaign for a new eco-friendly water bottle'.

The task has been decomposed into: 1. Market Research, 2. Creative Asset Production, 3. Channel Selection, and 4. Campaign Launch.

Analyze the complexity of this task tree: {"root": "Plan Trip"}

The complexity score is 1.5 with a parallelism potential of 1.0.

Check if these subtasks have circular dependencies: [Task A, Task B]

The task structure is valid with no circular dependencies detected.

Complexity is a weighted sum of word count, domain specificity, and ambiguity, which is then scaled by the decomposition depth.

Related Connectors