#learning

Career and workforce intelligence built on a deep HR ontology — skill taxonomies, role definitions and responsibilities, compensation and incentive structures, learning and development pathways, sourcing strategies, and role/skill evolution mapping. This structured foundation, combined with a RAG knowledge base curated from 50+ premium sources (HBR, McKinsey, BCG, Gartner, Forrester) and updated 3x daily with live web research, powers 6 guided skills and 42 MCP tools for two audiences: working professionals getting personalized career intelligence (CV optimization, salary benchmarking, career strategy), and HR/TA teams running structured talent evaluation, candidate shortlisting, compensation analysis, and consulting-grade workforce research reports. Example Use Cases (for HR/TA teams): 1. Custom Evaluation Models — Train CareerProof on your organization's existing assessment rubrics, scorecards, and evaluation criteria to build custom eval models that evaluate candidates through your specific lens. Upload your competency frameworks and historical assessments, then run inference on new candidates — scored and ranked exactly how your team would, at scale. 2. Candidate Evaluation & Shortlisting — Set up a hiring context with company profile and job description, upload candidate CVs, then batch-rank them with GEM competency scoring and JD-FIT matching. Apply your custom eval models for organization-specific scoring, or deep-dive any candidate with a 360-degree evaluation including tailored interview questions derived from skill taxonomy analysis. 3. Workforce Research Reports — Generate consulting-grade PDF reports across 16 types (salary benchmarking, skills gap analysis, org design, DEI assessment, succession planning, sourcing strategy, and more). Each report is grounded in real-time market data from premium sources and structured around HR ontology — role definitions, compensation structures, L&D pathways, and skill evolution mapping. 4. Compensation & Incentive Benchmarking — Get market-calibrated salary and total compensation intelligence for any role, location, and industry. Analysis is structured around compensation and incentive frameworks from the HR ontology, enriched with live web research and curated knowledge base data covering base salary, equity, bonuses, and benefits. Example Use Cases (for the working professional or career coach): 1. Career Intelligence Chat (Hyper-Personalized) — Ask career strategy questions and get hyper-personalized responses that fuse your CV context with deep insights from the career and workforce RAG knowledge base. Salary benchmarks calibrated to your function and location, industry disruption analysis mapped to your skill profile, and career pivot recommendations grounded in role evolution data — not surface-level answers, but intelligence drawn from the same sources that inform executive strategy. 2. CV Optimization (Hyper-Personalized) — Upload your CV and receive a hyper-personalized positioning pipeline that combines your actual experience with deep insights from our career and workforce RAG knowledge base. Market analysis calibrated to your industry and seniority, career opportunity identification grounded in role/skill evolution data, and targeted edits with trade-off analysis — not generic advice, but intelligence shaped by 50+ premium research sources and your unique career trajectory.

Learning repository: conversations and notes from my learning sessions with Claude. And all files are upload by Claude MCP server

A server for managing contextual data in DICOM tools, supporting medical imaging and machine learning workflows.

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Learn MCP by building from Scarch

The repo is based on Model Context procotol of Python SDK, including DL models in CV, and provide the abilities to the LLM or vLLM model

Learning how to build mcp-servers to connect in llms via langchain

Turn slow PyTorch into fast CUDA/Triton kernels. 32 parallel swarm agents optimize your code on real datacenter GPUs (B200, H200, H100, A100) with up to 14x speedup over torch.compile.

Uses Claude or OpenAI API to convert prompt-mapped input into concrete MCP server components such as tools, resource templates, and prompt handlers.

MCP server for training Linear Regression Model.

A minimal agentic app to interact with OLLAMA models leveraging multiple MCP server tools using BeeAI framework.

Persistent memory MCP for AI agents — Knowledge graph + Hebbian learning + Anti-hallucination. 12 tools, 1 dependency, zero manual setup.

Model Context Protocol (MCP) with Gemini 2.5 Pro. Convert conversational queries into flight searches using Gemini's function calling capabilities and MCP's flight search tools

My first MCP (Model Context Protocol) server

KinielaGPT es un servidor MCP (Model Context Protocol) diseñado para potenciar tus predicciones de la Quiniela mediante un análisis avanzado de datos. Combina las probabilidades oficiales de LAE con un análisis contextual profundo: histórico de enfrentamientos, rachas recientes, clasificación y rendimiento como local o visitante. Ofrece tres estrategias de predicción, detección de sorpresas y un análisis pormenorizado partido a partido.

An attempt to create an MCP server so I can learn more about it

Convert topics or existing files into professional presentation videos with automated slides and narration. Streamline content creation by generating outlines, scripts, and high-quality text-to-speech audio in a single workflow. Manage the entire production pipeline from initial rendering to final video export for polished results.

学习模型上下文协议(MCP)服务器 - 翻译和学习资源

My first MCP Server for learning

An open-source MCP (Model Context Protocol) server that enables AI agents to record and retrieve learning activities through xAPI-compliant Learning Record Stores

Analyzes user-described build features (e.g. database, API integration, tools) and extracts core server requirements like resources, tools, prompts, external systems, and transports needed for MCP.

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Model Context Protocol (MCP) with Gemini 2.5 Pro. Convert conversational queries into flight searches using Gemini's function calling capabilities and MCP's flight search tools

Personal Context Technology for AI Personalization MCP server

Let AI agents like ChatGPT & Claude use real-world local/remote tools you approve via browser extension + optional MCP server

Final unified interface for global users. Just describe your server, paste your API key (Claude/OpenAI/Grok), and this library will build, test, and deploy the full MCP server locally or remotely.

A Model Context Protocol (MCP) server for agentic retrieval of financial data from Yahoo Finance. This server leverages YFinance to provide a simple and efficient way to access historical stock prices, dividends, stock splits, company information, and other financial metrics. This MCP server is designed to be used with various LLM applications that support the Model Context Protocol, such as Claude Desktop and Cursor. It allows users to retrieve financial data in a structured way, making it easy to integrate into AI applications.

Learning about MCP server

A Model Context Protocol (MCP) server for agentic retrieval and semantic search over unstructured and structured data using Milvus, a high-performance vector database. This server enables large language model (LLM) applications to efficiently index, store, and retrieve vector embeddings derived from diverse data sources—such as documents, images, metadata, or logs. By leveraging Milvus, the MCP server supports similarity search and contextual retrieval, enabling intelligent access to relevant information based on natural language queries. Designed for integration with MCP-compatible clients, this solution provides a scalable, low-latency foundation for building AI-powered applications with contextual awareness and retrieval-augmented generation (RAG) capabilities.

This MCP server implemented with spring AI and Oracle DB is mainly focused in learning and testing purpose

Digimon Engine — Multi-Agent, Multi-Player Framework for AI-Native Games and Agentic Metaverse

Automatically generates a full MCP server codebase (Node/Python/Java) using the validated schema, outputting modular files for CLI-based execution or Claude Desktop connection.

CLI tool to scaffold, run, and test MCP servers locally with Node.js or Python, integrated with Claude and OpenAI key injection and auto-link to Claude Desktop.

Strata is a self hosted AI memory server. Your AI remembers everything across every session, on your own hardware. Key features: . Semantic search (find memories by meaning, not keywords) . Per-agent API keys with granular permissions . 3D constellation viewer with live agent activity . File vault (attach real documents to memories) . CSV audit log (full transparency on every agent action) . Pre-Strata history import (your memory doesn't start at install day) . Global MCP kill switch (emergency brake, only a human can undo) . Automatic deduplication . 10 structured thought types . Backend using PostgreSQL . Runs on a Raspberry Pi Always on, always local, always yours.

Persistent codebase intelligence that gives agents memory across sessions.

MCP Server Repository

This repo is dedicated to learning and working with large language models (LLMs), prompt engineering, and modern GenAI tools such as LangChain, RAG, and vector databases.

Generates optimal Claude/OpenAI-ready prompts to build each part of the MCP server (resources, tools, prompts) from the input generated by `mcp_input_analyzer`.

Enables any MCP-compliant agentic platform (Claude, Grok, TypingMind, Cursor, etc.) to auto-discover generated MCP servers and sync capabilities from build metadata.

Konnektoren MCP Server helps you learn German faster and smarter. Instantly find answers to grammar questions, discover the best exercises for your level, and create your own interactive challenges—all in one place. Whether you’re a student, teacher, or self-learner, this server gives you easy access to high-quality German learning resources and tools, right inside your favorite AI assistant. No technical setup required—just connect and start improving your German today!

Learning mcp server and clients through Documentations

A learning project for MCP server integration, between eventcatalog.io and cursor.

A powerful Model Context Protocol server for LinkedIn interactions that enables AI assistants to search for jobs, generate resumes and cover letters, and manage job applications programmatically.

A repository to learn MCP Server with implementing git features.

A comprehensive MCP server providing financial data through unofficial Yahoo Finance APIs for educational purposes. Features 20+ tools including real-time stock prices, financial statements, analyst recommendations, ESG scores, company profiles, and historical data. Built with Python and yahooquery library, offering production-ready error handling and cross-platform compatibility. Perfect for learning financial analysis, educational research, and market data exploration. Note: Uses unofficial APIs - not for commercial trading decisions.

Transform your AI agents into Apple development experts! Apple RAG MCP gives you instant access to official Swift docs, design guidelines, and comprehensive Apple platform knowledge through cutting-edge RAG technology. With professional AI reranking and hybrid search across iOS, macOS, watchOS, tvOS, and visionOS documentation plus Apple Developer YouTube content, you'll get precise, contextual answers every time. Compatible with Cursor, Claude Desktop, and all MCP tools - start building smarter Apple apps today!

Deep Learning capstone project about MCP servers