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"Fetching historical data from tardis.dev" matching MCP servers:

  • A
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    Not graded
    quality
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    maintenance
    Indexes local files (PDF, TXT, CSV, Markdown) with embeddings for semantic search. Provides both CLI and MCP server interfaces so Claude Desktop can search and read your local documents.
    MIT
  • A
    license
    Not graded
    quality
    D
    maintenance
    A Model Context Protocol (MCP) server that enables LLMs to interact directly the documents that they have on-disk through agentic RAG and hybrid search in LanceDB. Ask LLMs questions about the dataset as a whole or about specific documents.
    18
    78
    MIT
  • F
    license
    A
    quality
    B
    maintenance
    askDB is an MCP server that retrieves relevant database schema (DDL) from a Pinecone index and provides it to LLMs to write SQL, without connecting to the database itself.
    3
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    license
    A
    quality
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    maintenance
    A server that provides data retrieval capabilities powered by Chroma embedding database, enabling AI models to create collections over generated data and user inputs, and retrieve that data using vector search, full text search, and metadata filtering.
    13
    585
    Apache 2.0
  • A
    license
    A
    quality
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    maintenance
    Enables AI agents to interact with Milvus vector databases and Zilliz Cloud through natural language, allowing users to create clusters, manage collections, insert vector data, and perform semantic searches directly from their AI assistants.
    16
    34
    Apache 2.0
  • F
    license
    B
    quality
    D
    maintenance
    Connects AI clients to MindsDB via the MySQL protocol to execute SQL queries, manage databases, and perform semantic searches within knowledge bases. It enables automated workflows through job scheduling and provides seamless integration with external data sources.
    11
  • A
    license
    B
    quality
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    maintenance
    Enables fetching relevant content and embeddings from Supavec via the Model Context Protocol, allowing AI assistants like Claude to access vector search capabilities.
    2
    9
    4
    MIT
  • A
    license
    C
    quality
    D
    maintenance
    Enables AI agents and users to query, analyze, and manage Teradata databases through modular tools for search, data quality, administration, and data science operations. Provides comprehensive database interaction capabilities including RAG applications, feature store management, and vector operations.
    39
    MIT
  • A
    license
    Not graded
    quality
    A
    maintenance
    MCP server for Vectros, a typed multi-tenant record store with hybrid search and citation-grounded RAG, enabling agents to query, search, and ask questions over their own indexed data.
    345
    1
    Apache 2.0
  • A
    license
    Not graded
    quality
    C
    maintenance
    Enables interaction with KDB.AI through natural language for vector database operations, similarity searches, hybrid search, and advanced data analysis.
    1
    Apache 2.0
  • A
    license
    Not graded
    quality
    C
    maintenance
    An interface for managing and querying MariaDB databases that supports standard SQL operations alongside advanced vector and embedding-based search capabilities. It enables AI assistants to seamlessly integrate relational and vector data workflows through a standardized protocol.
    189
    MIT
  • A
    license
    Not graded
    quality
    D
    maintenance
    A Machine Control Protocol (MCP) server that enables storing and retrieving information from a Qdrant vector database with semantic search capabilities.
    Apache 2.0
  • A
    license
    Not graded
    quality
    D
    maintenance
    An MCP server that provides persistent semantic memory backed by PostgreSQL and pgvector for storing and searching thoughts via vector embeddings. It enables dimensional organization, conflict detection, and historical tracking of facts, decisions, and observations.
    17
    AGPL 3.0
  • A
    license
    Not graded
    quality
    D
    maintenance
    An MCP server for RAG using Qdrant that automatically indexes documents from directories and generates search tools for each collection.
    MIT