Qdrant MCP Server

The mcp‑server‑qdrant is a fully‑featured Model Context Protocol server that turns the Qdrant vector search engine into an AI‑friendly semantic memory layer. By exposing simple “store” and “find” tools, it lets language‑model assistants persist arbitrary text as embeddings and retrieve the most relevant snippets later—exactly what developers need to give LLMs a persistent, context‑aware working memory.

At its core, the server offers two declarative tools.

• ingests a string of information, optionally enriched with JSON metadata, and writes it to a specified Qdrant collection (or the configured default). The tool automatically generates an embedding with a chosen provider and model, ensuring that every stored piece of data is searchable by semantics rather than plain text.
• accepts a natural‑language query, performs an embedding‑based similarity search against the chosen collection, and returns the closest matches as distinct messages. The result set can be fed back into a conversation or workflow, allowing the assistant to “recall” earlier facts or documents on demand.

Developers benefit from a few key advantages:

• Standardized interface: By following MCP, any LLM platform that supports the protocol can instantly interact with Qdrant without custom adapters.
• Semantic search out of the box: The server handles embedding generation and similarity ranking, freeing developers from implementing these steps themselves.
• Configurable persistence: Whether you run Qdrant locally via a file path or against a hosted cluster, the same environment variables control the connection.
• Extensible metadata: Optional JSON tags let you add context such as author, source, or timestamps, which can be queried later for fine‑grained filtering.

Typical use cases include:

• AI‑powered IDE assistants that remember project‑specific patterns or code snippets and can retrieve them during coding sessions.
• Chatbots that maintain a knowledge base of user preferences or prior interactions, enabling more personalized conversations.
• Custom AI workflows where downstream steps need to fetch related documents, logs, or training data without manual lookup.

Because the server is built on FastMCP, it inherits a robust set of environment‑variable configurations for logging, tracing, and security, making it easy to integrate into existing CI/CD pipelines or containerized deployments. In short, mcp‑server‑qdrant gives AI developers a plug‑and‑play semantic memory layer that scales with their application and stays true to the MCP standard.