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"Connecting external data sources for contextual LLM calls" matching MCP servers:

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    A Model Context Protocol server that provides Retrieval-Augmented Generation capabilities using Contextual AI, enabling AI interfaces like Cursor IDE and Claude Desktop to query domain-specific knowledge with context-aware responses and source citations.
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    21
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    Provides RAG (Retrieval-Augmented Generation) capabilities via Contextual AI, enabling query processing and context-aware responses with citations. Integrates with MCP clients like Cursor IDE and Claude Desktop.
    1
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    Integrates Dify's external knowledge base API with the Model Context Protocol to enable AI agents to retrieve and query relevant information. It supports relevance scoring, metadata filtering, and flexible configuration through environment variables or command-line arguments.
    1
    15
    MIT
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    MCP server for grounded, cited AI: answers questions from live web sources, verifies claims, fact-checks documents, searches and reads URLs, summarises, classifies, and extracts fields, with usage tracking and status.
    MIT
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    Enables creation of persistent, compounding knowledge bases using Karpathy's LLM Wiki pattern with LLM-maintained markdown wikis. Supports automated ingestion, cross-referencing, synthesis, and linting of sources as an alternative to traditional RAG systems.
    61
    MIT
  • A
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    A Model Context Protocol server that provides unified access to multiple LLM APIs including ChatGPT, Claude, and DeepSeek, allowing users to call different LLMs from MCP-compatible clients and combine their responses.
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    31
    16
    MIT
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    Evaluates RAG outputs on faithfulness, answer relevancy, and context precision using an LLM-as-a-Judge backend. Exposes tools for running evaluations, scoring individual samples, and checking thresholds, enabling CI gating and on-demand assessment via MCP.
    MIT
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    MCP server that enables persistent, hybrid, local memory for LLM agents, with vector + BM25 search, knowledge graph, and policy-driven retention, providing token-budgeted context injection for AI assistants.
    MIT
  • F
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    Enables AI agents to query structured data refined from unstructured web sources, including developer breaking changes, B2B pricing matrices, regulatory compliance, semantic search, and on-demand URL refinement.
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    MCP for Azure DevOps Boards is a MCP server that lets your favourite AI browse, query and update Azure DevOps work items as if it were a project manager. Written in Rust and optimized for tokens usagem, It runs via stdio or HTTP mode and uses standard Azure authentication with az login.
    24
    6
    MIT
  • A
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    A semantic layer query engine with MCP support, enabling AI assistants to query structured data through natural language and declarative interfaces.
    2
    Apache 2.0
  • A
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    Enables Claude to automatically extract entities and relationships from URLs, PDFs, and YouTube videos to build structured knowledge graphs in Neo4j. It supports custom schemas, academic citation extraction, and community detection for advanced research and content analysis.
    Apache 2.0
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    Enables asking questions and performing semantic searches against an Inkeep knowledge base. Deployable to Vercel with Next.js.
    1
    MIT