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"A server to build a knowledge base of Node.js packages with RAG search functionality" matching MCP servers:

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    Provides AI assistants with tools to grade, generate, and validate UI components against the components.build specification. Supports searching documentation, checking compliance, and generating framework-agnostic accessible components.
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    Apache 2.0
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    Combines a knowledge graph with RAG (Retrieval-Augmented Generation) capabilities for semantic code indexing and search. Enables creating entity relationships, managing observations, and performing semantic searches across indexed codebases.
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    An MCP server that wraps the local Grok Build CLI, enabling Codex to delegate code reviews, bounded coding tasks, and setup diagnostics to Grok for a second opinion or parallel processing.
    4
    Apache 2.0
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    A fully local RAG MCP server for semantic code search and code intelligence, using AST-level chunking and hybrid search to pinpoint functions, classes, and APIs. No cloud, no API keys, zero setup.
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    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.
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    MIT
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    Enables semantic search and call-graph exploration over decompiled Unity Mono C# codebases, with hybrid retrieval, LLM re-ranking, and lazy-loaded project indexes.
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    A semantic code search MCP server that enables natural language queries against your codebase, supporting features like related file discovery and context expansion, all running locally.
    2
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    A local-first MCP server that indexes code repositories and enables AI assistants to search, fetch, and navigate code using BM25 hybrid retrieval, supporting common languages and tools like Codex and ChatGPT.
    8
    MIT
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    Enables AI assistants and developers to analyze code for language-specific best practices and idiomatic patterns across programming languages, CI automation, and configuration formats.
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    MIT
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    Enables token-efficient semantic search and analysis over any directory of files through hybrid search, directory overview, structural analysis, and dependency graphs.
    14
    MIT