Valkey MCP Task Management Server

The Valkey MCP Task Management Server is a purpose‑built backend that brings structured task planning into the world of agentic AI. By exposing its functionality through the Model Context Protocol, it allows Claude‑style assistants to create, update, and track tasks as if they were native part of the assistant’s own memory. This solves a common pain point for developers: persisting complex, hierarchical task data without having to build custom adapters or maintain separate databases.

At its core the server offers a lightweight REST‑style interface that maps directly to MCP verbs. Plans can be created, read, updated, or deleted; within each plan tasks inherit ordering, priority, and status metadata. Notes can be attached to both plans and tasks using Markdown, giving agents the ability to store rich context that can later be surfaced or edited. All of this data lives in Valkey, a fast key‑value store that ensures low latency and durability. The Go implementation keeps the footprint small while still providing robust error handling, making it suitable for both local dev and production deployments.

Key capabilities include:

• Function listing via or a JSON call, letting an agent discover the full set of actions it can perform.
• Function invocation through or the generic endpoint, enabling agents to manipulate plans and tasks on demand.
• Transport flexibility with Server‑Sent Events for real‑time updates, Streamable HTTP for streaming JSON responses, and STDIO for lightweight process communication.
• Health checks to integrate with orchestration tools or CI pipelines.

Typical use cases involve building autonomous project managers, where an AI assistant can negotiate task priorities with a user, schedule follow‑ups, and update status as work progresses. In educational settings, the server can host learning plans that evolve based on student interaction, while in DevOps it can track incident response steps and link them to monitoring alerts. Because the server’s API is protocol‑agnostic, it can be paired with any AI platform that supports MCP, making it a versatile bridge between human intent and persistent task data.