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    MCP Compass is a discovery and recommendation service that assists AI assistants in finding and understanding Model Context Protocol servers through natural language queries.
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    134 npm
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    MIT
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    A complete MCP server for Retrieval-Augmented Generation with file management and vector memory for agents. Supports multiple document formats (PDF, DOCX, TXT, MD, CSV, JSON) with semantic search using Hugging Face embeddings and ChromaDB for efficient vector storage.
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    10 npm
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    MIT
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    An interactive learning assistant that helps developers understand AI-generated code changes through quizzes and blocking learning sessions. It tracks and searches debugging experiences using RAG to ensure users build long-term technical understanding rather than just copy-pasting solutions.
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    MIT
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    Enables AI assistants to access contextual knowledge from multiple repositories through Sequa's Contextual Knowledge Engine. Provides architecture-aware code understanding and cross-repository context for more accurate, production-ready code generation.
    76 npm
    MIT
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    A Model Context Protocol server that enhances AI agents by providing deep semantic understanding of codebases, enabling more intelligent interactions through advanced code search and contextual awareness.
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    MIT
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    This read-only MCP Server allows you to connect to JSON data from Claude Desktop through CData JDBC Drivers. For full CRUD support, check out our MCP Server for JSON (https://www.cdata.com/drivers/json/download/mcp).
    MIT
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    Connects MCP clients to databases like Snowflake, BigQuery, and Databricks through Alkemi's data platform, enabling natural language database queries with proper schema understanding and metadata management for consistent team-wide access.
    51 npm
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    MIT
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    An MCP server that gives AI agents structured code understanding and precise code intelligence via local indexing of AST, call graphs, and semantic search.
    42 npm
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    Apache 2.0
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    Enables fusing and reranking results from multiple search or retrieval sources using Reciprocal Rank Fusion and hybrid rank fusion. It provides deterministic scoring, robust parsing, and structured JSON output for autonomous agent workflows.
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    Enables AI agents to retrieve precise structural and semantic context from codebases using hybrid search across 166+ languages. Supports offline/local operation and automatic fallback for resilient, token-efficient code understanding.
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    Apache 2.0
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    Enables natural language search and analysis of uploaded PDF, CSV, and Excel documents using retrieval-augmented generation and MCP tools, providing contextual answers to user queries.
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    MIT
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    An intelligent codebase processing server that provides agentic RAG capabilities for code repositories, enabling semantic search and contextual understanding through self-evaluating retrieval loops.
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    MIT
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    A lightweight, zero-configuration MCP server for source code analysis with GraphRAG capabilities, enabling structural understanding and efficient code completion from MCP-compatible AI tools.
    94 PyPI
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    MIT
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    Converts natural language descriptions into validated CanvasXpress JSON configurations for chart generation using RAG and a built-in visualization knowledge base. It supports over 70 chart types and ensures generated configs are compatible with provided data headers and column types.
    1
    MIT
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    MCP server for querying PanDA documentation, analyzing job errors, and retrieving JSON data using multiple AI models.
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    Apache 2.0