#rag

基于七牛云产品构建的 Model Context Protocol (MCP) Server，支持用户在 AI 大模型客户端的上下文中通过该 MCP Server 来访问七牛云存储资源、利用 Dora 服务进行图片操作等。 如果有什么需求欢迎在下方评论，您也可以在 github 仓库中提 issue。

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MCP Server for Yahoo Finance (STATUS: PRE-ALPHA)

A mcp server to retrieve documents from ragflow

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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.

A RAG-ready MCP server for semantic PDF search with OCR, FAISS, and transformers—plug into any MCP client and retrieve intelligent answers within your MCP client.

MCP server for RAG-based document search and management

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LocalMind is an local LLM Chat App fully compatible with the Model Context Protocol. It uses Azure OpenAI as a LLM backend and you can connect it to all MCP Servers out there.

A Browser Automation Tool for Humans and AI - Built with Playwright, optimized for Bun runtime, supporting CLI and MCP Server modes for webpage analysis and automation

Desktop AI Agent builder supports for local/remote LLMs, RAG, MCP

One simply siphons the excess thoughts from one's mind, pours them into the basin, and examines them at one's leisure. It becomes easier to spot patterns and links, you understand, when they are in this form.

Key-Value storage MCP server based on unjs/unstorage, support drivers: redis, mongodb, filesystem. http

A demo of Retrieval-Augmented Generation (RAG) application with MCP server integration.

A simple MCP server to interact with Pure storage arrays

Stop AI hallucinations with certified, source-backed facts. Swiss Truth MCP gives your AI agent access to a curated knowledge base covering Swiss law, health, finance, climate, AI/ML, and more. 8 tools, no API key needed, public and free.

Distribute Artifacts Across OCI Registries With Ease

Scrape any webpage as clean Markdown and extract YouTube transcripts. Enterprise grade infrastructure at $0.001 per request.

AI-Ready Data & Context Engineering API. Connect any data source — PostgreSQL, CRMs, APIs — and get clean, structured, AI-ready data in seconds. Natural language queries, semantic vector search, automated PII redaction, deduplication, and AI-powered context building for RAG pipelines. 25 free credits on signup.

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Advanced MCP Server Setup with uv, llama-index, ollama, and Cursor IDE

Diseño e Implementación de interconexión entre LLM y Espacios de Datos mediante Model Context Protocol (MCP)

MCP File System Server for Claude Desktop

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

A Model Context Protocol (MCP) server implementation for RAG

Create AI-powered video workflows including automatic editing, content moderation, voice cloning, highlight generation, and searchable video moments—all accessible via simple APIs and intuitive chat-based interfaces.

"primitive" RAG-like web search model context protocol (MCP) server that runs locally. ✨ no APIs ✨

A learning repository exploring Retrieval-Augmented Generation (RAG) and Multi-Cloud Processing (MCP) server integration using free and open-source models.

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AI-native Postgres + REST API + auth + storage + static sites + serverless functions. Pay with x402 USDC on Base. No humans, no signups.

MCP server for object storage, supporting a range of operations for object storage

MCP Server to interact with Google Cloud Storage

Storacha MCP storage server - self-sovereign data for your AI applications.