cjo4m06/mcp-shrimp-task-manager
Mcp Shrimp Task Manager
This project is a framework for managing multi-agent software development workflows built on the Model Context Protocol. It functions as an AI-driven task orchestrator that decomposes complex development objectives into atomic units, tracks their lifecycle, and coordinates specialized agents to execute, verify, and refine work. By maintaining persistent project context and history, the system ensures continuity across sessions, allowing agents to retain state and adhere to established coding standards.
The system distinguishes itself through its dependency-graph task management and multi-agent coordination capabilities, which allow it to balance workloads and delegate tasks based on agent expertise and role categorization. It integrates semantic codebase indexing and search to map natural language intent to project resources, while providing a graphical interface for visualizing task dependencies and monitoring agent performance. Security is maintained through strict schema-validated request handling, resource-based permissions, and mandatory user approval for system-level command execution.
Beyond core orchestration, the platform supports extensive configuration through custom prompt templates, dynamic parameter injection, and project-wide standards enforcement. It includes robust file system operations for content inspection and structured editing, alongside web retrieval capabilities to supplement local context with real-time information. The system provides comprehensive observability through task audit trails and performance analytics to ensure transparency throughout the development lifecycle.
Features
- Agentic Task Orchestrators - Orchestrates complex development workflows by decomposing objectives into structured, executable tasks for AI agents.
- Multi-Agent Coordination Systems - Coordinates specialized AI agents by delegating tasks based on expertise and managing inter-agent dependencies.
- Model Context Protocol Implementations - Implements the Model Context Protocol to enable AI agents to interact with local files, research, and task queues.
- Multi-Agent Collaboration Systems - Enables multiple specialized AI agents to collaborate within shared workspaces for complex task execution.
- Role-Based Agent Orchestration - Coordinates multiple agents by assigning specific roles and operating procedures to complete complex development objectives.
- Persistent Context Management - Preserves project progress, history, and coding rules across sessions to ensure AI agents maintain continuity.
- Task Decomposition Systems - Provides methods for breaking down complex objectives into actionable sub-tasks for automated agents.
- Task Delegation - Routes incoming tasks to the most suitable agent by evaluating complexity and technical requirements.
- Agentic Task Lifecycles - Manages the lifecycle of complex goals decomposed into persistent graphs of dependent agent tasks.
- Task Execution - Implements and tracks actionable steps derived from analysis to ensure work meets project standards.
- Agent Security Runtimes - Enforces security policies, sandboxing, and permission controls for automated agents to protect sensitive information.
- Dependency Graph Managers - Models and resolves task relationships in complex software projects to ensure logical execution order.
- Feature Lifecycle Tracking - Monitors the progress of software capabilities and tasks from initial discovery through to completion.
- Agentic File Editing - Enables AI agents to autonomously modify files using structured context markers for accurate integration.
- Workload Balancers - Automatically redistributes tasks from overloaded agents to underutilized ones to maintain overall system efficiency.
- AI Prompt Engineering Templates - Defines reusable instruction templates and standards to ensure consistent, high-quality outputs from AI-driven tools.
- Input Validation Schemas - Enforces structural constraints and data consistency on incoming requests using type-based schema definitions.
- Semantic Search - Performs search functionality that understands the intent and context of queries rather than just keywords.
- Dynamic Parameter Injection - Provides logic for injecting runtime parameters into task execution templates to ensure context-aware instruction processing.
- Semantic Code Indexing - Maps source code structures using semantic search and call-graph traversal to provide context for AI agents.
- Agent Task State Persistence - Preserves the internal state and progress of agent tasks to ensure continuity across sessions.
- Shell Command Execution - Triggers and controls operating system shell commands with mandatory user approval for security.
- System Settings Managers - Provides a centralized interface for managing global system settings, including API keys and storage paths.
- Execution Audit Trails - Captures the full task breakdown, sub-task outputs, and final refinement into a structured audit trail for project progress.
- Template Management Systems - Offers a markdown-based system for versioning and managing instruction templates that dictate agent behavior.
- Visual Task Management Boards - Provides visual interfaces for tracking development progress, task status, and workflow dependencies.
- API Access Restrictions - Enforces token-based authorization and request rate limits to secure system communication and protect capacity.
- AI Agent Role Assignments - Assigns specific development roles like architect or tester to AI coding agents within a project.
- Software Development Lifecycle - Automates the software development lifecycle through AI-driven reasoning, file manipulation, and task verification.
- Multi-Agent Coordination Frameworks - Manages interactions and prevents conflicts between autonomous agents in a development workflow.
- Project Rule Enforcement - Enforces project-wide coding standards and architectural constraints through configuration-driven rules for automated agents.
- Specialized Agent Collections - Delegates specific development tasks to curated collections of specialized agents based on defined roles and expertise.
- Agent Performance Monitoring - Provides analytical reports on agent utilization and system performance to optimize development workflows.
- Pipeline History Managers - Maintains a searchable record of past task executions to enable reflection and context preservation.
- Intent Clarification - Uses specialized agent-driven loops to gather missing information from users and refine implementation plans.
- Request Schema Validators - Verifies incoming instructions against defined schemas to prevent processing errors and ensure system consistency.
