Project MCP Server

Project MCP Server – Structured Project Management for AI Assistants

The Project MCP Server fills a critical gap for teams that want to embed deep, persistent project knowledge into AI workflows. Traditional project management tools expose flat task lists or spreadsheets, but they rarely provide a machine‑readable, graph‑based representation that an AI can query and reason over. By exposing a rich knowledge graph of projects, tasks, milestones, resources, risks, and more, the server lets AI assistants act as real‑time project advisors: they can ask for the next high‑priority task, retrieve risk mitigation plans, or generate status reports that reflect the latest decisions.

At its core, the server stores every project entity in a durable graph. Each node—whether it’s a task, milestone, resource, or teamMember—carries typed relationships such as depends_on, assigned_to, and has_status. This structure allows the AI to traverse dependencies, compute critical paths, or evaluate resource bottlenecks without manual data manipulation. The Persistent Project Context feature guarantees that the graph survives across sessions, enabling long‑term trend analysis and historical decision tracing.

Developers benefit from a single, well‑defined tool: startsession. When invoked, it spawns a new project management session, assigns a unique ID, and surfaces the current state of all projects. The tool automatically resolves has_status and has_priority relations, highlighting tasks that are ready for action. This hands‑off approach lets developers embed the server into chat‑based assistants, voice interfaces, or automated dashboards with minimal friction.

Real‑world scenarios that thrive on this server include agile product teams needing instant sprint planning, construction managers monitoring milestone compliance, or R&D groups tracking experimental risks. In each case, the AI can answer “What are the next blocking tasks?” or “Which resources are over‑allocated?” by querying the graph, thus reducing context switching and accelerating decision making. The server’s Decision Logging capability ensures that every choice is recorded with context, supporting audit trails and knowledge transfer.

Unique advantages of the Project MCP Server lie in its blend of persistence, relational depth, and AI‑ready tooling. By treating projects as interconnected graphs rather than flat tables, it unlocks advanced inference—such as detecting cascading risk impacts or automatically scheduling dependent tasks. For developers building AI‑powered PM assistants, this server offers a turnkey knowledge backbone that scales with project complexity and integrates seamlessly into existing MCP workflows.