Search Results for "layer"

The Qdrant MCP Server is an official Model Context Protocol server that integrates with the Qdrant vector search engine. It acts as a semantic memory layer, allowing for the storage and retrieval of vector-based data, enhancing the capabilities of AI applications requiring semantic search functionalities. ​

...Rather than acting as a thin converter, it’s built as a native FastAPI extension that understands dependency injection, so you can reuse Depends() for authentication and authorization across your MCP tools. The server speaks directly to your app over its ASGI interface, avoiding extra HTTP hops between the MCP layer and your API, which reduces latency and simplifies deployment. A tiny bootstrap is enough to stand up an MCP server and, if desired, mount an HTTP transport for remote clients. The docs emphasize a FastAPI-first workflow: keep your schemas, reuse your middleware, and surface endpoints to agents without rewriting controllers. The project is active, with examples and a dedicated site that shows getting started, security, and transport options.