MCP server for retrieving context from a Qdrant vector database
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# mcp-server-qdrant: A Qdrant MCP server [](https://smithery.ai/protocol/mcp-server-qdrant) > 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. This repository is an example of how to create a MCP server for [Qdrant](https://qdrant.tech/), a vector search engine. ## Overview An official Model Context Protocol server for keeping and retrieving memories in the Qdrant vector search engine. It acts as a semantic memory layer on top of the Qdrant database. ## Components ### Tools 1. `qdrant-store` - Store some information in the Qdrant database - Input: - `information` (string): Information to store - `metadata` (JSON): Optional metadata to store - `collection_name` (string): Name of the collection to store the information in. This field is required if there are no default collection name. If there is a default collection name, this field is not enabled. - Returns: Confirmation message 2. `qdrant-find` - Retrieve relevant information from the Qdrant database - Input: - `query` (string): Query to use for searching - `collection_name` (string): Name of the collection to store the information in. This field is required if there are no default collection name. If there is a default collection name, this field is not enabled. - Returns: Information stored in the Qdrant database as separate messages ## Environment Variables The configuration of the server is done using environment variables: | Name | Description …
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