Qdrant MCP

Qdrant MCP

A Model Context Protocol (MCP) server that exposes the full [Qdrant](https://qdrant.tech) vector database API as tools — collection management, advanced hybrid search, payload and vector editing, snapshots, and server observability. Not just store/find.

Table of Contents

• Overview
• Key Features
• Requirements
• Installation
• PyPI
• Docker
• Claude Desktop bundle (.mcpb)
• Quick Start
• Connecting to Claude
• Claude Desktop / Claude Code (local, stdio)
• Remote (streamable-http)
• Text Search
• Prompts
• Configuration Reference
• Available Tools
• Read-Only Mode
• Architecture
• Security
• Development
• Versioning & Changelog
• License

Overview

Qdrant MCP is a thin MCP server that wraps the Qdrant API one-to-one: it registers a tool per Qdrant operation, validates the input with Pydantic, calls the official qdrant-client SDK, and returns the result. It never parses documents and never decides how to chunk text — it is not a RAG pipeline, on purpose. A client can bring its own vectors, or — with the optional embeddings extra — send plain text and search by meaning or by keywords (see Text Search).

That is what sets it apart from the official Qdrant MCP server, which exposes exactly two tools (store/find). This server covers the rest of Qdrant's surface — everything under collections, points, search, payload, indexing, snapshots, and observability — so an LLM client can manage a Qdrant deployment end to end, and query it in plain text too.

See ROADMAP.md for the full phase-by-phase design history, including every finding that shaped a decision (in Spanish).

Key Features

• Text search — semantic search with plain text (query_text, embedded locally with fastembed using the model the collection declares), BM25 keyword search with stemming and stopwords (bm25_query, Qdrant 1.20, no embeddings needed), and both fused in one hybrid query. Upserts accept text too. Works on collections built elsewhere once their model is declared.
• Full collection & point lifecycle — create/update/delete collections, CRUD on points, scrolling, counting.
• Every Qdrant search mode — plain vector search over dense, sparse and multivectors, hybrid search (RRF — optionally weighted — and DBSF fusion), nested multi-stage prefetch (fuse dense+sparse, then rerank with a multivector), MMR diversity, custom score formulas (payload boosts, recency and geo decay), ordering by a payload field, random sampling, grouped queries, recommend, discover, and pairwise distance matrices. Search accuracy knobs (search_params, score_threshold, paging, timeouts) are accepted by every search tool.
• Payload & vector editing — set/overwrite/delete/clear payload, payload indexes, facet counting, named (dense/sparse) vector management, ordered batch operations.
• Snapshots — per-collection and full-storage backup/restore.
• A search advisor — qdrant_collection_doctor checks a collection (status, index, memory, aliases, text setup) and answers each finding with the exact tool call that fixes it; qdrant_search_quality_eval measures recall@k of the approximate search against an exact one, and latency, for any search_params variants — so tuning HNSW or quantization is judged by numbers, e.g. "quantized without rescoring: recall 0.97; with rescoring: 1.0".
• Guided workflows — MCP prompts that walk a model through a whole task: tune a collection's search (diagnose, measure, propose, wait for approval), rebuild a collection without downtime, or add text search to an existing collection. In Claude Code they're slash commands, e.g. /mcp__qdrant__tune_search docs. The server also hands the model a short orientation (its MCP instructions), built from the toolsets you enabled.
• Aliases, reindexing & introspection — switch the collection a name points to without downtime, copy points between collections server-side (vectors never pass through the model), check shard layout and whether the optimizer has finished indexing, and profile the payload keys a collection actually has before writing a filter.
• Observability — telemetry, Prometheus metrics endpoint, resource quotas, self-diagnosed issues.
• Opt-in tool surface — the 58 tools are grouped into toolsets you enable explicitly, so a client isn't handed all of them by default (only the 13-tool core toolset is on).
• Read-only guard — one flag removes every mutating tool from the registry entirely.
• Easy on the model's context — the schemas the server advertises are compact (Qdrant's verbose Filter and configuration models are summarized, validation unchanged), so the default toolset costs ~47 KB of schema instead of ~150 KB; with_payload can name just the keys to return (["title", "url"]), since payload is most of what a model reads back; and a per-call result cap (QDRANT_MCP_MAX_LIMIT) stops one mistaken limit from flooding the conversation.
• Resilient by default — every Qdrant call goes through retry-with-backoff and returns Qdrant's own error message on failure, never a generic exception.

Requirements

• Python 3.12+
• Qdrant server v1.19.0 or newer; tested against v1.20.0. Two tools (qdrant_collection_vector_create/qdrant_collection_vector_delete) depend on an endpoint that returns 404 on older Qdrant servers (verified against v1.13.6 and v1.15.1) — everything else works on older versions. Tool descriptions state the behavior verified on v1.20.0, and say so where v1.19 differed (alias batches, MMR with offset). Text search needs v1.16+ (it keeps the model in the collection's metadata), and its BM25 mode v1.20+.

Installation

PyPI

# Run without installing (recommended for Claude Desktop/Code configs)

uvx mcp-qdrant

Or install into your environment

pip install mcp-qdrant

With text search (adds fastembed; models are downloaded on first use)

uvx --from 'mcp-qdrant[embeddings]' mcp-qdrant

Docker

docker run --rm -p 8000:8000 \

-e QDRANT_URL=http://host.docker.internal:6333 \ -e QDRANT_MCP_TRANSPORT=streamable-http \ -e QDRANT_MCP_HTTP_HOST=0.0.0.0 \ -e QDRANT_MCP_SHARED_SECRET= \ -v mcp-qdrant-models:/models \ ghcr.io/avaazquezz/qdrant-mcp:latest

The image only makes sense with streamable-http — stdio needs a client to own the process's stdin/stdout directly, which a detached container can't provide. The server binds 127.0.0.1 by default, so QDRANT_MCP_HTTP_HOST=0.0.0.0 is required for the port to be reachable from outside the container. Images are published on every tagged release as {version}, {major}.{minor}, and latest.

Claude Desktop bundle (.mcpb)

Download mcp-qdrant.mcpb from the latest release and double-click it. Claude Desktop installs the server via uv (resolving dependencies on your machine, no Python installation required) and prompts for Qdrant URL, API key, local path, toolsets, and read-only mode through its own settings form — no JSON to edit.

Quick Start

Point the server at a local Qdrant instance over stdio and confirm it's reachable:

QDRANT_URL=http://localhost:6333 mcp-qdrant

Once connected from an MCP client, a typical first exchange looks like:

1. `qdrant_health_check` — confirms the configured Qdrant instance is reachable before doing anything else.
2. `qdrant_collection_create` — {"collection_name": "docs", "vector_size": 4, "distance": "Cosine"}.
3. `qdrant_points_upsert` — {"collection_name": "docs", "points": [{"id": 1, "vector": [0.1, 0.2, 0.3, 0.4], "payload": {"title": "hello"}}]}.
4. `qdrant_query` — {"collection_name": "docs", "query_vector": [0.1, 0.2, 0.3, 0.4], "limit": 5}.

The full parameter shape of every tool is described in its own MCP schema — an LLM client reads those directly via tools/list; the table in Available Tools below is a human-readable summary of the same data.

Connecting to Claude

Claude Desktop / Claude Code (local, stdio)

claude_desktop_config.json (Claude Desktop) or .mcp.json (Claude Code):

{

"mcpServers": { "qdrant": { "command": "uvx", "args": ["mcp-qdrant"], "env": { "QDRANT_URL": "http://localhost:6333", "QDRANT_MCP_TOOLSETS": "core,search" } } } }

Or use the .mcpb bundle described in Installation — same result, no JSON to edit.

Remote (streamable-http)

For a server reachable over the network (e.g. added as a custom connector in Claude.ai) with a single, fixed backing Qdrant. QDRANT_MCP_SHARED_SECRET is required in this mode — the server refuses to start as streamable-http without one, to avoid serving an unauthenticated endpoint over the network (verified hands-on: an open streamable-http server is trivially usable by anyone with the URL).

QDRANT_URL=http://localhost:6333 \

QDRANT_MCP_TRANSPORT=streamable-http \ QDRANT_MCP_HTTP_HOST=0.0.0.0 \ QDRANT_MCP_SHARED_SECRET= \ mcp-qdrant

In Claude.ai (Customize → Connectors → Add custom connector, verified hands-on against a real account): enter the server's HTTPS URL, then on the detected authentication screen choose "None" and add a Request header — Authorization → Bearer . (Authorization is used here because it's one of the two header names Claude.ai's custom-connector UI accepts without requiring Anthropic's manual approval of a custom name.)

Text Search

Text search needs no extra configuration — the collection carries it. Its metadata records which model made each dense vector and which payload field holds the text, so every later query and upsert uses the same model (a query embedded with a different model would return plausible-looking nonsense with no error):

{"mcp_qdrant": {"text_field": "text", "models": {"dense": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"}}}

• New collection — qdrant_collection_create with text_model (creates a dense vector of the model's size and records it) and/or text_language (adds a BM25 index on the text field: stemmed, accent-folded, stopwords removed).
• Existing collection — qdrant_collection_update with text_models ({" ": " "}, "" for the unnamed vector) declares the model its vectors were made with; the size is checked against the vector. For BM25, create a text payload index with "scoring": true (and a stemmer) on the text field with qdrant_payload_index_create.
• Write — qdrant_points_upsert with {"id": 1, "text": "..."} instead of a vector: one text