An MCP server that turns natural language into structured development tasks with persistent memory and chain-of-thought workflows for AI agents.
mcp-shrimp-task-manager is worth checking the docs before setup with strong trust signals. Check agent compatibility and use-case fit before adding it to your workflow.
Repository setup guidance
gh repo view cjo4m06/mcp-shrimp-task-manager --webOpen the official README and confirm the supported install method.
Add the server entry to your MCP client config.
Restart your agent and verify that the server tools appear.
Shrimp Task Manager helps AI coding assistants plan and track software projects step by step. You describe what you want in plain language, and it breaks the work into small tasks, remembers progress between sessions, and keeps the AI focused on what to do next. It works with tools like Claude Code and Cursor to make development more organized and efficient.
Shrimp Task Manager is an MCP (Model Context Protocol) server designed to enhance AI-powered software development. It provides a structured task management system that persists across sessions, enabling AI agents to maintain context and progress even when token limits are reached. Key features include intelligent task decomposition (breaking complex projects into manageable subtasks), dependency tracking, iterative refinement, and support for chain-of-thought reasoning. The tool converts natural language descriptions into structured development tasks, tracks dependencies between tasks, and supports continuous execution mode for autonomous development. It integrates seamlessly with MCP-compatible AI clients such as Claude Code, Cursor, Windsurf, and Cline. Shrimp also offers a web-based GUI for visual task management, template support for different languages, and project rule initialization to guide AI behavior. The project is open-source under the MIT license and has gained significant traction with over 2000 stars on GitHub.
Strong trust signals; still review the README and permissions before production use.
Last commit was about 340 days ago.
2111 GitHub stars indicate community interest.
41 open issues signal maintenance load.
MIT license detected.
Plan and execute a multi-step feature implementation by describing it in natural language, with automatic task breakdown and dependency management.
Maintain development context across multiple AI sessions, ensuring the agent remembers what was done and what to do next.
Use continuous mode to let the AI autonomously work through a task list, executing and verifying each step.
Initialize project rules and templates to enforce coding standards and style consistency across the codebase.
Track progress of complex projects with a visual GUI, viewing task status, dependencies, and completion percentages.
The tool requires file system write access to store task data; ensure DATA_DIR is set to a secure location.
Running in continuous mode may execute tasks autonomously; review generated code before deployment.
No built-in authentication; use in trusted environments only.
2,111
Stars
254
Forks
41
Issues
MIT
License
Local codebase intelligence CLI and MCP server for AI coding agents with change-safety gates and audit evidence.
An offline MCP server that indexes your codebase for semantic search, code search, and git history retrieval.
Official MCP reference servers from Anthropic. Includes servers for filesystem, GitHub, Postgres, Slack, and more.
An MCP server that turns natural language into structured development tasks with persistent memory and chain-of-thought workflows for AI agents.
AI-assisted developers using Claude Code, Cursor, or Windsurf who want structured task management., Software engineers building complex projects that require persistent context and step-by-step planning., DevOps engineers and team leads looking to integrate AI agents into their development workflow.
The setup section provides repository-level starting guidance, not a guarantee of an independently verified installation. Check the official README and release notes.
The tool requires file system write access to store task data; ensure DATA_DIR is set to a secure location. Running in continuous mode may execute tasks autonomously; review generated code before deployment.
Similar or complementary options to evaluate include roam-code, code-memory, MCP Server Collection.
Jun 6, 2026
3 security/trust notes recorded.
Setup difficulty is 3/5.