Best for
- AI-assisted development requiring long-term project context.
- Integration with MCP-compatible coding assistants like Claude Code and Cline.
- Developers wanting structured task management to increase efficiency.
S
Shrimp Task Manager
Community
Intelligent task management for AI-powered development with chain-of-thought and reflection.
GitHub source repository ↗
40FMRS · D
Shrimp Task Manager is an MCP server for AI agents, emphasizing chain-of-thought and reflection. It provides structured task decomposition, dependency tracking, and persistent memory. It is suitable for integration with MCP-compatible coding assistants, but requires user configuration and setup of a data directory.
Shrimp Task Manager is a task tool built for AI Agents, emphasizing chain-of-thought, reflection, and style consistency. It converts natural language into structured dev tasks with dependency tracking and iterative refinement, enabling agent-like developer behavior in reasoning AI systems. It is an MCP server that provides persistent memory, structured workflows, smart decomposition, and context preservation, helping AI agents avoid losing context or repeating work in software development.
Tools
init
Initialize project rules
plan
Create a task plan
execute
Execute a specific task
research
Enter research mode
reflect
Review and improve a task
list
List all tasks
Setup
- Ensure Node.js 18+ and npm/yarn are installed.
- Clone the repository: git clone https://github.com/cjo4m06/mcp-shrimp-task-manager.git
- cd mcp-shrimp-task-manager
- npm install
- npm run build
- Create a .mcp.json file in your project directory and configure the MCP server (see example in README).
- Start your MCP-compatible AI client (e.g., Claude Code) with the config file.
claude_desktop_config.json
{
"mcpServers": {
"shrimp-task-manager": {
"command": "node",
"args": [
"/path/to/mcp-shrimp-task-manager/dist/index.js"
],
"env": {
"DATA_DIR": "/path/to/your/shrimp_data",
"TEMPLATES_USE": "en",
"ENABLE_GUI": "false"
}
}
}
}
Fit and risk
Not for
- Simple interactions that don't need task management.
- Environments with strict security requirements for data location (you must configure DATA_DIR).
Required permissions
- File system access: needs to read/write task data files (specified by DATA_DIR).
- Command execution: the MCP server is launched via command line, so the client must allow executing node commands.
Risks and side effects
- Data persistence: task data is stored locally; regular backup recommended.
- Misconfiguration can prevent the server from starting.
- Dependence on the MCP implementation of the AI client may cause compatibility issues.
Troubleshooting
- Common checks: 1. Ensure DATA_DIR environment variable is set correctly. 2. Ensure the path to dist/index.js is correct. 3. Ensure Node.js version >=18. 4. Verify the client loads the .mcp.json properly.
Use cases
Feature development: decompose complex features into subtasks and implement step by step.
Bug fixing: analyze issues, create a fix plan, and execute automatically.
Research and learning: systematically compare technologies and create migration plans.
Supported clients
Claude DesktopFull support
ClineFull support