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"Kotlin RAG (Retrieval-Augmented Generation) implementation resources" matching MCP servers:

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    Kotlin MCP Server for Android app development using OpenAI, Gemini, or OpenRouter. Enables AI-assisted coding via Aider, Gradle build/test integration, Kotlin LSP, and Docker-based portability.
    31
    AGPL 3.0
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    LLM routing proxy that cuts API costs 60-90% by auto-selecting the cheapest capable model across OpenAI, Anthropic, and Google. Provides stats, config, and model comparison tools via MCP.
    21
    1
    MIT
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    MCP server that demonstrates the Resources feature by exposing static and dynamic resources, including contact data and personalized greetings, through MCP.
    MIT
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    A TypeScript MCP server that allows querying documents using LLMs with context from locally stored repositories and text files through a RAG (Retrieval-Augmented Generation) system.
    4
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    Provides local Retrieval-Augmented Generation (RAG) capabilities using Ollama for embeddings and ChromaDB for vector storage. It enables users to ingest and perform semantic searches across PDF, Markdown, and TXT documents within MCP-compatible clients.
    4
    245
    MIT
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    A Retrieval Augmented Generation system that enables AI assistants to perform semantic searches and manage document indices for markdown files. It supports PostgreSQL with pgvector and integrates both Google Gemini and Ollama for intelligent embedding generation.
    1
    MIT
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    An MCP server that generates AI agent tools from Postman collections and requests. This server integrates with the Postman API to convert API endpoints into type-safe code that can be used with various AI frameworks.
    1
    13
    MIT
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    Enables LLM agents to efficiently understand and navigate a codebase by providing semantic search over symbols and a reference graph, replacing expensive grep/glob calls with structured tools like definition lookup, caller/callee queries, and change-impact analysis.
    1
    MIT
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    Enables AI applications to access and contextualize organizational knowledge sources including GitHub repositories and internal documentation through standardized MCP protocol integration. Features OAuth 2.1 authentication, vector-based semantic search, and optimized context chunking for enterprise development workflows.
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    An MCP server that enables users to query Kedro framework documentation using retrieval-augmented generation. It builds a local knowledge base from documentation files to help users navigate and apply Kedro's data science pipeline framework.
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    Provides a complete end-to-end MCP server implementation with file system tools, web scraping capabilities, and system information access. Includes ready-to-use configuration files and integration examples for Claude Desktop, ChatGPT, and other AI models.
    6