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> The [Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you’re building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need.
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> The [Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction) is an open protocol that enables
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> seamless integration between LLM applications and external data sources and tools. Whether you’re building an
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> AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to
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> connect LLMs with the context they need.
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This repository is an example of how to create a MCP server for [Qdrant](https://qdrant.tech/), a vector search engine.
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-`query` (string): Query to retrieve a memory
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- Returns: Memories stored in the Qdrant database as separate messages
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## Installation
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## Installation in Claude Desktop
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### Using uv (recommended)
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### Using mcp (recommended)
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When using [`uv`](https://docs.astral.sh/uv/) no specific installation is needed to directly run *mcp-server-qdrant*.
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When using [`mcp`](https://github.com/modelcontextprotocol/python-sdk) no specific installation is needed to directly run *mcp-server-qdrant*.
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