MCP server
Qdrant MCP server
Qdrant's official MCP server gives AI a semantic memory: store information in a Qdrant collection and find it again by meaning.
- Official server
- Databases
At a glance
- Maintained by
- Qdrant
- Tools exposed
- 2 listed below
- Reviewed
- September 24, 2026
Why it matters
What it does and why it matters
Qdrant is a vector database used for semantic search and retrieval. Its official MCP server is deliberately simple: one tool stores information, another finds it by meaning. Together they give an assistant a searchable memory, such as notes, snippets or knowledge it should be able to recall later.
For teams building knowledge assistants, it is a quick way to let an assistant both add to and search a shared knowledge store.
Storing can be turned off with a read-only setting, which we use when the collection is curated by people and the assistant should only search it. The server embeds text with a configurable model, so choose one that matches the rest of your search setup.
Its simplicity is the point. Two tools are easy to understand, easy to explain to a team and easy to secure. That makes Qdrant a good first step before building a full retrieval pipeline with chunking, reranking and access rules for each document. When the prototype proves useful, we move loading into a separate, reviewed process and keep the assistant on search only.
Use cases
Best business use cases
Team memory
Semantic search
Code snippets
Support answers
RAG prototypes
Tools
Available tools
Tool names as published by the maintainer at review time. Versions change, so check the repository for the current list.
Read and write: Read and write (store and find); QDRANT_READ_ONLY disables storing.
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qdrant-storeStores information and metadata in a Qdrant collection.
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qdrant-findFinds relevant stored information by semantic search.
Workflows
Example workflows
Build a support answer library
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1
Support leads store approved answers through the assistant.
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2
Each answer is embedded and saved.
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3
Agents later ask a customer question.
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4
The assistant finds the closest approved answers.
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5
The agent adapts and sends the reply.
Search-only assistant
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1
Load a curated collection separately.
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2
Start the server in read-only mode.
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3
Staff ask questions.
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4
The assistant searches the collection.
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5
It answers with the matching entries.
Prompts
Example prompts
“Remember this: our refund window for annual plans is 30 days.”
“Find what we stored about onboarding enterprise customers.”
“Search for snippets about retrying failed webhooks.”
Setup
Installation and configuration
Examples for Claude Desktop and Claude Code. Replace the placeholder values with your own keys, and store secrets outside version control.
Claude Desktop: claude_desktop_config.json
{
"qdrant": {
"command": "uvx",
"args": [
"mcp-server-qdrant"
],
"env": {
"QDRANT_URL": "https://xyz-example.eu-central.aws.cloud.qdrant.io:6333",
"QDRANT_API_KEY": "your_api_key",
"COLLECTION_NAME": "your-collection-name",
"EMBEDDING_MODEL": "sentence-transformers/all-MiniLM-L6-v2"
}
}
}Claude Code: terminal
claude mcp add code-search -e QDRANT_URL="http://localhost:6333" -e COLLECTION_NAME="code-repository" -- uvx mcp-server-qdrant- Local server: stdio (also sse and streamable-http). PyPI: mcp-server-qdrant (uvx mcp-server-qdrant).
- Needs: QDRANT_URL (or QDRANT_LOCAL_PATH); QDRANT_API_KEY; COLLECTION_NAME; Optional: EMBEDDING_MODEL, QDRANT_READ_ONLY.
Security
Security considerations
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Read-only mode
Turn off storing for curated collections.
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API key
Use a key scoped to the needed collection.
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What gets stored
Decide what the assistant may store, and avoid personal data.
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Review entries
Review stored entries regularly.
Related servers
Servers that pair well with it
Chroma
Chroma's official MCP server lets AI create collections, add, query, update and delete documents in a Chroma vector database, local or cloud.
Pinecone
Pinecone's official developer MCP server lets AI search Pinecone docs, manage indexes, upsert records, search and rerank.
Memory
The MCP project's Memory reference server gives AI a local knowledge graph to store people, things and facts and recall them later.
Keep exploring
Solutions and services that use it
Related services
View all related services- MCP integration Connect AI assistants to your CRM, files, databases and tools through Model Context Protocol servers, with safe permissions.
- Database design Data models that stay fast and correct as your business grows, with backups and access rules in place.
- AI Product Development AI agents, knowledge assistants, copilots and document automation built into the way your team already works.
- Web applications Custom browser-based software for the work your team does every day, from booking tools to operations systems.
- Dashboards and reporting Dashboards that pull numbers from the systems you already use and show what matters without a spreadsheet.
- Multi-tenant architecture One product serving many customer accounts, with each account's data kept separate and secure.
Solutions
View all solutions- Automated reporting Reports that build themselves from your systems on schedule, with a plain-language summary of what changed.
- Knowledge assistant (RAG) Ask a question in plain words and get an answer from your own documents, with links to the sources.
- Forecasting dashboards Forecast demand from your sales history and seasonality, and flag items to reorder before they run out.
- Internal help desk assistant An assistant in Slack or Teams that answers policy and how-to questions from your handbooks and opens tickets when needed.
- Payroll automation Turn pay rules, attendance and leave into tested software so the monthly run becomes a review and payslips go out in one batch.
- AI sales research agent Short, sourced briefings on each prospect before a call, drafted by an AI agent from public information and your CRM.
Industries
View all industriesCase studies
View all case studiesGuides and articles
View all guides and articles- How to connect AI to your database safely Let staff ask questions of your data in plain English without risking production systems or sensitive records.
- PostgreSQL vs MySQL The two most popular open-source databases compared for business applications.
- What is RAG and when do you need it? How retrieval-augmented generation lets AI answer from your documents, when it fits and how to build it well.
- How to automate reporting with AI and MCP Build reports that assemble themselves from your systems through MCP, with a written summary people can trust.
- How to set up automated reporting Replace manual weekly and monthly reports with dashboards and summaries that update themselves.
- RAG vs fine-tuning Two ways to make AI work with your knowledge, compared by cost, accuracy and upkeep.
AI models
View all ai models- text-embedding-3-large OpenAI's most capable embedding model for search and retrieval, with 3,072-dimension vectors and support for English and other languages.
- text-embedding-3-small OpenAI's efficient, lowest-cost embedding model, with 1,536-dimension vectors for search, retrieval and similarity at scale.
- Cohere Embed v4 Cohere's multimodal embedding model for enterprise search, embedding text, images and mixed documents, with flexible vector sizes and long inputs.
- Voyage 4 Voyage AI's current embedding series, now part of MongoDB, with large, standard and lite models that share one embedding space.
- Embeddings Models that turn text into vectors for search, retrieval, clustering and recommendations.
- Gemini Embedding 2 Google's current embedding model, multimodal: it embeds text, images, video, audio and PDFs for search and retrieval.
Glossary terms
View all glossary terms- RAG RAG, or retrieval-augmented generation, is a method where an AI system first finds relevant passages in your documents, then answers using only those passages.
- Database A database is an organized store of data that software can search, update and keep consistent, such as customers, orders or bookings.
- Vector database A vector database stores embeddings, lists of numbers that represent meaning, and quickly finds the ones most similar to a query.
- Embeddings Embeddings are lists of numbers that represent the meaning of text or images, so software can find items that are similar in meaning.
- Semantic search Semantic search finds results by meaning rather than exact words, so a search for "reset my login" can find an article titled "Forgot your password".
- Backend The backend is the part of an application that runs on servers, storing data, applying business rules and serving the frontend through APIs.
FAQ
Questions people ask us
Have a question that is not here? Ask us directly.
It stores information in a Qdrant collection and finds it again by meaning.
Yes. A read-only setting disables storing.
Yes. It is maintained by Qdrant.