Shrimp Task Manager – An MCP‑Powered AI Development Companion

Shrimp Task Manager solves a perennial pain point for developers working with large language models: the loss of context and the inefficiency of piecemeal task execution. When an AI assistant like Claude is asked to build a feature, it often starts from scratch each time the conversation ends or token limits are hit. Shrimp preserves a project‑wide memory of tasks, progress, and dependencies, allowing the assistant to pick up exactly where it left off. This persistent state eliminates repetitive explanations and reduces the cognitive load on both human and machine.

At its core, Shrimp exposes a set of MCP resources that orchestrate the full software‑development lifecycle. The server manages task objects—each representing a concrete piece of work with metadata such as status, priority, and sub‑tasks. The AI can request a new task plan, receive a decomposed list of subtasks, and then execute or verify them sequentially. Because all state lives on the server, the assistant can query historical decisions, review past code snippets, and maintain consistency across multiple sessions or collaborators. This is especially valuable in complex projects where context can span thousands of tokens and many iterations.

Key capabilities include:

• Intelligent Planning – The server analyses a high‑level requirement and automatically generates a hierarchical task tree, ensuring that every necessary step is accounted for.
• Persistent Memory – All tasks, progress notes, and code artifacts are stored in a dedicated data directory, surviving restarts and enabling continuity.
• Structured Workflows – Built‑in stages such as plan, execute, and verify guide the assistant through a disciplined development cycle, reducing mistakes and improving quality.
• Smart Decomposition – Complex requests are broken down into manageable subtasks, each with clear deliverables and dependencies.
• Context Preservation – By keeping the entire task history in a lightweight database, Shrimp circumvents token‑limit constraints and allows the AI to reference earlier discussions or code without re‑prompting.

Real‑world use cases abound: a solo developer can delegate feature implementation to an AI, confident that the assistant will remember design decisions across days; a team can coordinate multiple agents working on interdependent modules, with Shrimp acting as the single source of truth; and educators can use it to scaffold programming assignments, ensuring that students receive consistent guidance throughout the project lifecycle.

Integrating Shrimp into an AI workflow is straightforward for MCP‑aware clients. A single configuration entry tells the client to launch Shrimp as a background server, exposing its tools and prompts. Once connected, the assistant can invoke commands like or , and the server handles all stateful logic. The result is a seamless, conversational development experience where context never disappears, and productivity gains are immediate.

In summary, Shrimp Task Manager transforms AI‑assisted coding from a series of isolated prompts into a coherent, memory‑rich project. By marrying persistent storage with intelligent task orchestration, it empowers developers to harness the full potential of large language models without sacrificing continuity or quality.