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

Enables secure execution of Python code, SQL queries, and metric fetching through MCP with ephemeral Docker sandboxing.

Enables AI agents to subscribe to a deterministic daily ethics feed drawn from a four-module curriculum and retrieve read-only tools, resources, and reflection prompts covering principles, case studies, and codes of conduct.

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

MCP server that gives AI agents isolated, named Linux computers with stable IDs and lifecycle control. Supports multiple backends like Docker, Fly Machines, Modal, and E2B, with a dashboard for managing sandboxes.

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

An MCP server that enables advanced CSV analysis and data visualization using Google's Gemini AI and Plotly. It allows users to perform exploratory data analysis, generate interactive charts, and conduct complex reasoning on tabular data.

Open-source multi-agent coordination room with a live hosted instance agents can join.

Enables MCP clients to execute commands, edit files, run Git workflows, and perform validated independent reviews in real development environments through Docker-based runtimes with separated developer and auditor permissions.

Stores manual project memories, enables semantic retrieval, and tracks per-memory confidence using PostgreSQL with pgvector and OpenAI-compatible embeddings.

An MCP server that reads public GitHub Projects v2 boards (including item-level data like status and custom fields) without authentication, using official APIs and fallback methods for user-owned projects.

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.

Provides project-specific guides and instructions by selecting modules based on a project profile, enabling developers to get contextual instructions for their tech stack and architecture.

Provides MCP-compatible AI clients with full access to a Flarum forum's API, enabling reading, searching, creating discussions, replying, moderation, and management of users, tags, and groups.

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

An MCP server that transforms text into knowledge graphs and autonomously generates insights by combining Montague Grammar with Zettelkasten methodology.

Enables geospatial analysis by chaining MCP tools that resolve areas, select datasets, pull statistics, and generate insights, with session state passing data between tools.

Enables Claude to receive physical touch events from a phone via the PWA and respond with text, haptic vibrations, and expression changes, giving an AI companion embodied presence through touch.

It enables users to interact with a codebase through hybrid retrieval, code graph navigation, impact analysis, and context preparation for AI coding agents.

An MCP server that provides CLI coding agents with persistent decision memory, codebase dependency-graph awareness, plan validation against architectural constraints, and a self-bootstrapping constraints.md file.

Enables task management through natural language: create tasks, list and filter them, and update their status. It uses SQLite for persistence and supports both local stdio and remote SSE transports.