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mcp shrimp task manager

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Basic Information

MCP Shrimp Task Manager is an intelligent task management system built on the Model Context Protocol (MCP) that provides a structured programming workflow framework for AI Agents. It is designed to guide agents through planning, analysis, execution and verification steps to implement code changes and development tasks systematically. The project bundles prompt templates, a set of agent tools and a long-term task memory mechanism to reduce repetition and preserve knowledge across sessions. It targets integration with MCP-compatible clients such as Cursor IDE and can be run as a Node.js/TypeScript MCP server. The repo also includes an optional React-based Task Viewer web UI and configuration guidance for global or project-specific deployments. Overall it acts as infrastructure to coordinate agent behaviors, enforce project rules, and assist developers in agent-driven code work.

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1478
App URL
https://github.com/cjo4m06/mcp-shrimp-task-manager
Github Repository
https://github.com/cjo4m06/mcp-shrimp-task-manager/blob/main/README.md

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App Details

Features
The repository documents and implements a range of agent-focused features for systematic development workflows. It supports task planning and in-depth analysis, automatic task decomposition and dependency management, execution status tracking and task completeness verification. The system evaluates task complexity and generates automatic summaries on completion. It provides a research mode for multi-source technical investigation, a task memory function that backs up execution history, and project rules initialization to standardize development practices. Available tools include plan_task, analyze_task, split_tasks, execute_task, verify_task and others. Configuration supports environment variables for prompt customization, support for the ListRoots protocol for project isolation, optional web GUI enablement, and a React Task Viewer for visual task management.
Use Cases
Shrimp Task Manager helps teams and developers by structuring how AI Agents approach programming work, reducing duplicated effort and preserving institutional knowledge through automatic task backups. Its task memory and automatic summaries allow future planning agents to reference prior solutions and mistakes, improving efficiency and consistency. Research Mode assists agents in exploring technologies and comparing approaches before planning work. Project rules initialization helps onboard contributors and enforce coding standards. Integration with MCP clients and Cursor IDE enables project-scoped or global deployments with project isolation via ListRoots. Prompt customization and the optional GUI let teams tailor agent behavior and monitor progress visually, while tools for planning, execution and verification support end-to-end agent-driven task lifecycles.

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