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RAG Systems

Tools and platforms for Retrieval-Augmented Generation (RAG), integrating external data sources with language models. Enables context retrieval, relevant document fetching, hybrid search, and dynamic knowledge injection into AI/LLM workflows.

MCP ServersBrowse all →

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    An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
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    MIT
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    Provides access to Leadpipe's intent and data APIs for topic discovery, audience management, and visitor data lookup. It enables agents to automate lead generation workflows, analyze website topics, and manage tracking pixels through natural language.
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    Gives your AI persistent memory across conversations. Stores facts automatically, finds them by meaning using hybrid search with query expansion, and organizes everything into topics without manual tagging.
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    An MCP server that enables web search and page content retrieval via the Keenable API, supporting search with filters and fetching clean markdown content from indexed URLs.
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    MCP search and evidence tool for AI agents. Rewrites queries, zooms into source domains, and returns sourced answers with metrics.
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    Enables GitHub Copilot and other LLMs to execute read-only SQL queries against Oracle databases with secure connection pooling and schema introspection capabilities.
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    Licensed, rights-cleared content for AI agents, 17 tools to discover, license, retrieve, and verify expert content with on-chain proof and EU AI Act Article 53 support.
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    Connect AI assistants to Manticore Search. Execute SQL queries, list tables, get schemas, and fetch documentation. Perfect for building RAG applications and search-powered AI agents.
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    Apache 2.0
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    Enables Claude and other MCP-compatible agents to process documents, extract structured data, detect PII, and export LLM-ready datasets through natural language tool calls.
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    Fetch published podcast transcripts as clean Markdown with real speaker names via the Spoken API. Tools: search_podcasts, get_transcript, get_balance.
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    Provides specialized AI-powered comprehensive research and analysis capabilities by integrating with advanced deep research agents, offering unlimited queries with no rate limits and faster performance than comparable services.
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    Enables AI agents to interact with Milvus vector databases and Zilliz Cloud through natural language, allowing users to create clusters, manage collections, insert vector data, and perform semantic searches directly from their AI assistants.
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    Adds persistent memory to AI assistants by connecting to the Memphora cloud platform, allowing them to store and recall facts across conversations. It enables tools for searching memories, extracting insights, and maintaining long-term user context and preferences.
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    A server that provides data retrieval capabilities powered by Chroma embedding database, enabling AI models to create collections over generated data and user inputs, and retrieve that data using vector search, full text search, and metadata filtering.
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    Apache 2.0
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    Embedded, local-first agent memory: facts extracted into a per-namespace SQLite file (vec0 + FTS5) with hybrid retrieval and point-in-time (time-travel) queries. ADD-only history over stdio — no server process, no cloud dependency.
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    Ratary is an AI Brain Platform — infrastructure that gives AI: Persistent memory — durable, owner-scoped, versioned Structured knowledge — metadata, relations, graph traversal Intelligent retrieval — hybrid search + bounded context assembly Protocol access — Ratary MCP, REST, optional gRPC It sits between AI clients and storage. One brain, many surfaces — Cursor, Claude Code, custom agents, enter
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    Keyless, local MCP server bringing ISTQB / OWASP / IEEE / ISO / EU AI Act QA standards into your AI client. Standards-grounded retrieval, deterministic QA effort estimation, automated QA document quality review (0-100 rubric), and JUnit/CSV test-results flakiness analysis.
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    Enables LLMs to interact with Zvec vector database through tools for collection management, document operations, vector search, and AI-powered embeddings.
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    Model Context Protocol server for YDB databases that enables AI-powered database operations and natural language interactions with YDB instances from any LLM that supports MCP.
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