"Using AI Techniques with MCP for Project Assistance" matching MCP servers:

Provides AI assistants with a standardized interface to interact with the Todo for AI task management system. It enables users to retrieve project tasks, create new entries, and submit completion feedback through natural language.

Enforces engineering governance for AI-driven software projects, ensuring state over prompt, freeze over generate, and audit over output through the 5S workflow.

Project Management AI - MCP server providing AI-powered tools and automation by MEOK AI Labs

MCP server that enables LLMs to read and analyze Microsoft Project schedules, including critical path, resources, and advanced construction planning layers (AWP and LPS) for work packages and Lean planning.

Enables AI assistants like Claude to create and manage projects, todos, priorities, and statuses through natural language, bridging the gap between AI and project management workflows.

A bridge between MCP Host applications and mem0 cloud service, specialized for project management with capabilities to store, retrieve, and search project information within a structured format.

Provides tools for managing project knowledge graphs, enabling structured representation of projects, tasks, milestones, resources, and team members.

MCP server for structured Laravel project planning. Scans projects, creates tickets, and exports as JSON.

Project Memory stores decisions, evidence, outcomes and reviewed lessons in local SQLite. Assistants retrieve bounded context through three MCP tools. People follow and plan work in a live workspace with an overview, sprint board, decision history, readable documents, skills and project maps. Offline exports remain available.

Provides AI-driven project memory management through structured prompts that help Claude parse tasks from specs, review code changes, sync with commit history, and maintain project documentation without directly accessing files.

Intent-based project documentation server that automatically maps natural language queries to the right sources (plans, todos, roadmap, docs) and provides comprehensive task management with dependency tracking, backlog promotion, and archival workflows.

Enables task management (create, list, update tasks with priority and status) using SQLite storage via MCP tools.

Facilitates AI session handoffs and next steps tracking through project-based organization, supporting task prioritization and seamless workflow management.

MCP server for managing hierarchical project tracking with PostgreSQL. Enables AI agents to create, read, update, and delete projects, epics, stories, summaries, status updates, context, and issues.

MCP server built into Rillsoft Project 10 for multi-project resource planning (local, Windows)

Enables Claude and other AI agents to build, edit, and analyze Microsoft Project desktop schedules on Windows through natural language, covering tasks, WBS, dependencies, resources, calendars, baselines, critical path, resource leveling, and earned value, with live changes in Project and single-step undo. It also exports results to PDF or CSV and reads project health and time-phased data for S-curves and histograms.

Enables any MCP-capable coding agent to share one plain-Markdown project state — goals, tasks, decision logs, and session handoffs — stored in a project's .apm/ directory, with lock-safe concurrent writes and consistency checks that flag stale, blocked, or unowned work. This lets multiple agents and a human CLI resume work across interrupted sessions without a database or external service.

A focused project memory system that provides knowledge graph capabilities and SQLite database operations for intelligent entity tracking and workflow management. It enables AI models to maintain persistent project context through 17 specialized tools, 10 resources, and 27 pre-built prompts.

A local, provider-neutral MCP server for repository-scoped issue handling. It provides a guarded interface to Linear, GitHub Issues, GitHub Projects v2, and Jira Cloud, with preview/apply safety and host-local configuration.

Enables any MCP-capable agent to maintain a shared .apm Markdown source of truth for project goals, tasks, decisions, handoffs, and health insights. It lets multiple agents and CLI/dashboard tools coordinate using the same file-based read/write logic without a database.