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"Agentic RAG: Understanding or Exploring Its Meaning and Applications" matching MCP servers:

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    A multi-agent Retrieval-Augmented Generation system exposed as an MCP server. Ask a question and a LangGraph pipeline plans the retrieval, pulls evidence from a pgvector knowledge base, optionally augments it with live web research, drafts a cited answer, and then self-critiques it for grounding — revising until the answer is supported by the sources.
    3
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    MIT
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    Read-only MCP server that exposes the agentic-os governance, SDLC, and Quality Engineering methodology to any MCP host. It never writes to your repository and never executes code — it serves the methodology, plans an install, and verifies it, handing any commands back to the host to run.
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    Apache 2.0
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    Provides comprehensive financial analysis and real-time market data including stock information, options chains, technical indicators, financial statements, earnings calendars, and market sentiment indicators through integration with yfinance and other financial APIs.
    14
    MIT
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    Score your agent's governance (0-100), lint MCP tool definitions, and estimate costs across all major models. Free diagnostic tools with no API key needed. Expert skill files on governance, economics, and system architecture available with free tier.
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    MIT
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    MCP server for persistent, cross-session, local-first memory for AI agents, storing memories as Markdown files with SQLite indexing for hybrid search.
    24
    Apache 2.0
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    MCP server for managing Bitcoin, Liquid Network, and Lightning wallets through AI assistants, with unified seed support, secure encrypted storage, and both CLI and tool interfaces.
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    MIT
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    Local RAG system for Claude Code with hybrid search (semantic + BM25), cross-encoder reranking, markdown-aware chunking, and 12 MCP tools. Zero external servers, pure ONNX in-process.
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    MIT
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    MCP server that integrates Apache Ranger authorization with RAG, enforcing Ranger policies to control access to knowledge bases before forwarding queries to RAG Studio.
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    Apache 2.0
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    Enables indexing local documents (PDF, Markdown, text, code) into a knowledge base and querying them via semantic search using local embeddings, all running privately on your machine.
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    Machine-readable detection lookups for SIEM enrichment and AI agents. Query 800+ LOLBAS and GTFOBins binaries plus process parent-child baselines — get risk levels, abuse categories, and MITRE ATT\&CK mappings without embedding data in prompts.
    6
    Apache 2.0
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    Enables semantic search and question answering over a knowledge base using hybrid retrieval and grounded answers, all running offline with no API keys.
    4
    MIT
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    Enables ingestion and semantic search over text documents using PostgreSQL + pgvector and OpenAI-compatible embeddings, allowing any LLM agent to retrieve relevant chunks for grounded answers.
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    AGPL 3.0
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    A Model Context Protocol (MCP) server that provides a local-first RAG engine for your markdown documents. It uses a file-based Milvus vector database to index your notes, enabling LLMs to perform semantic search and retrieve relevant content from your local files.
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    Apache 2.0