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"Understanding Structured Thinking" matching MCP servers:

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    An MCP server that enables searching and retrieving ACL NLP conference papers from a Qdrant vector database using semantic search and structured filters like year, venue, and field of study.
    4
    MIT
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    Enables AI-powered analysis of Ethereum blockchain data through semantic search, natural language queries, and structured filtering. Provides comprehensive access to addresses, transactions, blocks, tokens, and smart contracts with real-time blockchain intelligence.
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    MIT
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    Enables robots to store, retrieve, and consolidate episodic experiences including physical parameters, trajectories, and outcomes. It supports hybrid vector search with structured filtering and spatial sorting to help robotic agents learn from past successes and failures.
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    Apache 2.0
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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 minimal MCP server with four tools (add, greet, text_stats, divide) demonstrating typed parameters, structured outputs, and error handling over stdio transport.
    MIT
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    Structured agent memory system with hash-chain provenance, temporal decay, drift detection, provenance archaeology, vector embeddings, and git integration for OpenCode agents.
    Apache 2.0
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    An MCP server providing semantic memory storage and retrieval using vector embeddings powered by LanceDB and Google Gemini. It supports multi-tenant isolation and bucket-based organization for managing structured memories through natural language queries.
    MIT
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    A Python server that enables AI assistants to perform hybrid search queries against Apache Solr indexes through the Model Context Protocol, combining keyword precision with vector-based semantic understanding.
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    MIT
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    A production-grade MCP server and client implementation with comprehensive features including structured logging, health checks, metrics, authentication, and RAG capabilities with PostgreSQL vector search. Supports both stdio and SSE transports with containerization and security features for enterprise deployment.
    MIT
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    Provides persistent memory and semantic code understanding for AI assistants using MongoDB Atlas Vector Search. Enables intelligent code search, memory management, and pattern detection across codebases with complete semantic context preservation.
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    MIT
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    Enables enterprise document retrieval using graph-based reasoning and knowledge graphs. Allows agents to search and extract information from scattered documents through structured entity and relationship extraction.
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    Enables AI assistants to create and query multi-dimensional vector indexes from structured data files using Superlinked's vector search framework, supporting semantic search, recency, numerical, and categorical filtering.