{"id":"mcp-server-qdrant","name":"Qdrant MCP Server","homepage":"https://github.com/qdrant/mcp-server-qdrant","repo_url":"https://github.com/qdrant/mcp-server-qdrant","category":"data-search","subcategories":["vector-search","semantic-memory","knowledge-base"],"tags":["qdrant","vector-search","semantic-memory","embeddings","python","fastembed","mcp"],"what_it_does":"Official Qdrant MCP server that gives LLM agents a semantic memory layer: agents can store information with `qdrant-store` and retrieve semantically similar content with `qdrant-find`, backed by Qdrant vector database and FastEmbed embeddings.","use_cases":["Persistent semantic memory for AI coding assistants (store and retrieve code snippets, decisions, notes)","RAG augmentation: let an agent store ingested documents and search them during reasoning","Cross-session knowledge retention for long-running agentic workflows"],"not_for":["Exact keyword search (use Elasticsearch or similar full-text engines)","Structured relational queries requiring SQL-style filtering","Teams who do not already operate a Qdrant instance or are not willing to run one"],"best_when":"You need an agent to accumulate and semantically recall knowledge across sessions, and you already run (or are willing to run) Qdrant locally or in Qdrant Cloud.","avoid_when":"Your retrieval needs are primarily keyword-based or structured, or you want a zero-infrastructure managed memory solution without operating a vector database.","alternatives":["mcp-server-elasticsearch","mcp-memory","mem0-mcp"],"af_score":78.8,"security_score":75.0,"reliability_score":null,"package_type":"mcp_server","discovery_source":["github","npm"],"priority":"low","status":"evaluated","version_evaluated":"latest","last_evaluated":"2026-03-01T09:50:05.904094+00:00","performance":{"latency_p50_ms":null,"latency_p99_ms":null,"uptime_sla_percent":null,"rate_limits":null,"data_source":"llm_estimated","measured_on":null}}