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"How to Retrieve Data from Weaviate" matching MCP servers:

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    An MCP server for indexing and searching local text files using late-interaction retrieval (ColBERT-style MaxSim), enabling token-level relevance matching.
    2
    MIT
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    quality
    D
    maintenance
    An MCP server and document upload API designed for RAG workflows with Weaviate vector databases. It enables users to search, retrieve, and manage documents across specialized collections like account notes, product promos, and contracts.
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    Enables seamless integration with Weaviate vector databases, providing tools for semantic, keyword, and hybrid search across local or cloud instances. It supports schema management, collection retrieval, and multi-tenancy configurations through the Model Context Protocol.
    5
    MIT
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    A server that enables Claude AI to interact with Weaviate vector databases, supporting both search and storage operations through Anthropic's MCP protocol.
    2
    GPL 3.0
  • A
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    Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.
    3
    27
    Apache 2.0
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    maintenance
    Enables LLMs to query documents using semantic search, supporting PDFs, Word, Excel, and more. Organizes documents by topics from folder structure and provides advanced search features like phrase matching and date filtering.
    1
    MIT
  • F
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    quality
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    maintenance
    A Cloudflare Worker that transforms Cloudflare AI Search (AutoRAG) instances into an MCP server for querying documentation. It enables AI models to search and retrieve relevant information from custom document sets stored in R2 buckets.
    17
  • 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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    maintenance
    Enables natural language querying of PostgreSQL via MCP, combining SQL, vector search, and knowledge graph with automatic routing and token-aware curation.
    Apache 2.0
  • F
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    quality
    D
    maintenance
    Enables querying and analyzing user, product, and order data with filtering capabilities and real-time statistics. Supports WebSocket connections to XiaoZhi AI platform with automatic reconnection.
  • F
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    maintenance
    MCP server that ingests PDF documents into pgvector for semantic search and RAG pipelines. It handles extraction, chunking, local embeddings, and storage, enabling agents to make PDFs searchable via natural language.
  • F
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    maintenance
    Automatically routes natural language questions to RAG or Text2SQL paths to answer queries about virtual construction site data, supporting semantic search and structured aggregation.
  • F
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    quality
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    maintenance
    Translates plain English questions about infrastructure operations into SQL queries, executes them against a database, and returns the real answer.
  • F
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    quality
    A
    maintenance
    Enables local data discovery by indexing metadata from SQLite, CSV, and Markdown sources, providing hybrid keyword and TF-IDF semantic search via MCP tools (listSources, indexSource, search, getSchema) and a REST API.