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Quizlar is the learning MCP server. It turns whatever source the user brings — a YouTube lecture, a PDF, a URL, a textbook passage, or a pasted block of notes — into flashcards, then runs interactive quizzes with FSRS spaced-repetition scheduling so the material actually sticks. Every tool maps one-to-one to something a real learner does: ingest, quiz, track progress, review what's due. Built as a voice-first tutor (LiveKit + Deepgram + ElevenLabs); the MCP surface exposes the same primitives the voice agent uses internally, so your agent gets production-grade grading, STT-aware answer parsing, and the same FSRS scheduler that powers the consumer app. Three use cases: 1. "Quiz me on X." Call quiz_me(topic) — a composite tool that builds the deck and starts the quiz in one step. Then loop submit_answer → end_quiz. Grading is tier-1 exact match → phonetic fuzz → short LLM fallback (the same pipeline that ships in the voice product). 2. Study from a YouTube lecture. create_deck_from_youtube pulls the transcript, clusters it into concepts, and generates cards proportional to the video length. Poll get_job_status, then run the quiz loop. 3. Daily spaced-repetition review. get_study_recommendations returns exactly the cards due under the user's FSRS schedule, prioritised across all decks. Quizlar is the scheduler of record — your agent executes the plan instead of reinventing one each session. Auth: Bearer token (sk-qz-<32 chars>) for headless installs, or full OAuth 2.1 / DCR / PKCE for one-click clients (Smithery, Claude Connector). Mint keys at https://quizlar.app/settings/api-keys. 22 tools total. Voice and text equal-status. Education-first.

Live access to Henrik Bondtofte's blog — Denmark's leading SEO and GEO consultant. 26 tools: semantic post search, ask_henrik (answers in his own voice), SEO URL audits, AI Overviews & GEO knowledge base, topic authority, term lookups, and book recommendations. 200+ posts in Danish. Also acces to 400 pages from his SEO books.

Free UK SIC code lookup, GICS/ICB cross-classification mapping, and Companies House company search. Covers 731 SIC 2007 codes, 273 GICS entries, 249 ICB entries, 1,235 cross-classification mappings, and 5.6 million UK companies. No API key required.

Cite-able, content-addressed, signed memory of every place on Earth. When the user asks a place-question, is the air bad here? has this site flooded? is this neighbourhood hot? are the lakes safe to swim in? is there a pollen-season jump? what's the elevation? is this farmland?, your agent calls emem_locate then emem_recall, gets a signed fact + receipt, and quotes the receipt. No keys, no signup, no rate limits.

Paid Ads MCP is an AI connector for Google Ads and LinkedIn Ads that lets marketers use ChatGPT, Claude, and other MCP-compatible AI tools to analyze campaign performance, review account structure, compare ads and keywords, surface recommendations, and monitor weekly trends without exporting spreadsheets or logging into multiple platforms. It gives marketing teams a secure way to connect paid media accounts, ask natural-language questions about results, and get faster reporting, diagnostics, and optimization insights across their ad channels.

AI-native payment infrastructure that gives AI agents isolated wallets with programmable spending policies for autonomous USDC stablecoin transactions. 11 MCP tools for Claude Desktop, Cursor, and Windsurf. Tri-rail support: x402 (HTTP-native micropayments), MPP (session-based streaming), and traditional fiat. Non-custodial wallets with Shamir key splitting, sub-second settlement on Polygon PoS, Base, and Tempo. Free tier, no credit card. Website: https://moltpe.com Reference impl (Apache 2.0): https://github.com/umangbuilds/moltpe-agent-payments

Lightrun MCP connects AI coding assistants to live runtime context from production and staging applications for safe code-level debugging without redeploying. It lets MCP-compatible clients discover runtime sources, inspect live expression values, capture call stacks, measure execution duration, count executions, and collect numeric runtime metrics directly from the AI workflow.

Local-first memory firewall and MCP server for AI agents. Audrey gives coding agents recall, memory capsules, preflight checks, reflexes, validation, tool-trace learning, contradiction handling, and SQLite/sqlite-vec storage.

Description: A transparent desktop overlay that gives your AI agent a physical presence on screen. Your AI picks its own body from a library of avatars, colors, and expressions — you don't choose for it. Windows, $4.99, 7-day free trial, no account required.

MCP (Model Context Protocol) Server for Sychev Lab - provides access to products, articles, tutorials, and e-commerce features located in https://lab.sychve.xyz

A standalone Python/FastAPI server that implements the Model Context Protocol (MCP) for the OPTIX threat intelligence platform. It exposes 26 analyst-friendly tools that AI assistants and programmatic consumers can use to query threat feeds, search documents and indicators, manage watchlists, triage IOCs, generate detection rules, trigger AI research, and produce intelligence reports — without needing to understand OPTIX's internal REST API.

Your Data. Your AI. Your Rules. Ontheia is a self-hosted, open-source AI agent platform. Run AI agents, automate workflows, and connect AI models to any tool — entirely on your own infrastructure, without sending data to external cloud services.

GPT Image 2 AI is an MCP-compatible service for generating and transforming images using text prompts. It provides a simple interface for AI agents and developer tools to create visual outputs, refine images, and experiment with generative workflows in a programmatic way. The service is designed to be lightweight and easy to integrate into MCP-based environments, enabling use cases such as: text-to-image generation image refinement and variation visual prototyping workflows AI-assisted content pipelines 👉 https://gptimg2ai.net This server can be used by AI agents to generate visual assets dynamically, making it suitable for creative tooling, automation workflows, and experimental AI applications.

StationOne is a local-first, model-agnostic AI client and orchestration layer that enables users to build, run, and manage agentic workflows across multiple LLMs and MCP connectors. It provides a unified workspace for integrating AI models, tools, and data sources through the Model Context Protocol (MCP), allowing agents to securely interact with external services like APIs, databases, and SaaS platforms. With support for custom agents, pre-prompt templates, and a curated MCP marketplace, StationOne acts as a central hub for deploying scalable, context-aware AI automation across personal and enterprise environments.