"Guidance for Understanding Large Codebases and Learning Their Architecture" matching MCP servers:

A minimal MCP server that exposes Gemini's web and multimodal understanding (video, audio, document, image) through five dedicated tools.

Enables searching and retrieving NICE guidance for England — technology appraisals, clinical guidelines, diagnostics, interventional procedures, and quality standards — with full recommendation text, status, and supersession details.

Enables AI agents to maintain persistent, inspectable understanding through typed, revisable updates, and to coordinate multi-agent work via shared graph-based stigmergy.

Enables intelligent handling of large files through smart chunking, search with regex support, line navigation, and streaming capabilities without loading entire files into memory.

Enables downloading real binary files from Egnyte and uploading large files with chunked support, complementing the official Egnyte MCP connector.

Enables local video inspection and offline transcription with timestamped JSON and Markdown output, enforced filesystem boundaries, and reproducible content-addressed caching.

An agnostic MCP server that reads arc.yaml from the workspace, exposing tools to map layers, trace slices, generate scaffolding, and validate architectural rules.

Enables a browser agent to inspect, rank, preview, evaluate, and validate transformer layer pruning proposals, while ensuring only a person can commit removals.

A learning-oriented MCP server with tools for greeting, self-introduction, and name shouting, built with TypeScript and Zod validation.

Enables AI assistants to retrieve a project's architecture overview and file structure, auto-detecting monolith or multiproject layouts and exposing module listings to reduce token consumption.

An MCP server that generates comprehensive Learning Hour content for Technical Coaches, enabling teams to practice technical excellence through structured deliberate practice sessions.

Hey @roocode community! I'm thrilled to share a project born from my work with Roocode and the vision of an AI-powered development team: the Anubis MCP Server! This system is heavily inspired by Roocode and designed to orchestrate an AI development workflow based on agile methodology. It simulates

Guides users to official WCAG and W3C resources for learning web accessibility, without providing its own explanations.

A learning MCP server that transforms technical topics into four structured parts: definition, analogy, example, and hands-on try-it exercises, enabling beginner-friendly study directly from any MCP client.

A task manager MCP server that demonstrates core MCP concepts (resources, tools, prompts, stdio) and syncs data with a Next.js web app.

Enables AI agents to learn from their work by recording tasks, extracting patterns, detecting mistakes, and proactively surfacing insights, all using the agent's own model through a cooperative intelligence pattern.

An MCP server that gives AI coding assistants durable long-term memory, reusable skills, and evidence-based decision intelligence, running fully offline with no external API keys.

Implements cognitive science-backed interleaved learning techniques to create study plans, generate mixed-topic quizzes, manage flashcard decks, and track learning progress for optimal knowledge retention.

Provides stateful prompt optimization using research-backed techniques like APE and OPRO, learning from historical performance data via a vector database. It enables users to automatically refine prompts, retrieve high-performing examples, and track performance analytics through iterative feedback.

Enables MCP clients to connect to a privacy-first, self-hostable workout planning and training log, allowing coaching agents to preview and apply program changes while accessing training data through OAuth-protected endpoints.