mcp-server-qdrant
MCP server for retrieving context from a Qdrant vector database
Decision gist · record as of 2026-08-14
Yes, if you need MCP-compatible vector search integration. The package is straightforward to deploy, has low install friction, carries a permissive license, and has no known vulnerabilities. The aging maintenance status (247 days since last release) is a minor caution but not a blocker for stable, read-write workloads. Verify active support if you plan to rely on ongoing updates.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a running Qdrant instance (remote via QDRANT_URL or local via QDRANT_LOCAL_PATH) and Python >=3.10.
- fastembed will download its embedding model on first use.
- Low install friction with a pure-Python wheel and four runtime dependencies.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions, making it suitable for most deployment contexts.
last release 2025-12-10 (247 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,334,257 downloads/mo, #4,038 on PyPI
Alternatives
Verify before relying
# Install
pip install mcp-server-qdrant
# Set environment variables
export QDRANT_URL="http://localhost:6333"
export COLLECTION_NAME="my-collection"
export EMBEDDING_MODEL="sentence-transformers/all-MiniLM-L6-v2"
# Run the server
from mcp_server_qdrant import app
# Server exposes qdrant-store and qdrant-find tools via MCP- Whether the package is actively maintained or in maintenance-only mode beyond the aging signal
- Performance characteristics when handling large vector collections or high query volume
- Compatibility with specific Qdrant server versions or deployment modes
What it is and what it does
mcp-server-qdrant bridges LLM applications and Qdrant vector search by implementing the Model Context Protocol (MCP) standard. It runs as a standalone server that exposes two core tools: qdrant-store (to persist text and metadata into Qdrant collections) and qdrant-find (to retrieve semantically similar results via vector search). The server uses fastembed for embedding generation and can connect to either a remote Qdrant instance or a local database.
Configuration is entirely environment-variable driven, supporting multiple transport protocols (stdio, SSE, streamable-HTTP) and deployment modes including Docker and uvx. It integrates with Claude Desktop and other MCP clients, enabling AI applications to maintain and query a persistent semantic memory layer without direct database access.
Use it for
- Integrate Claude Desktop with Qdrant to give AI assistants persistent semantic memory across conversations
- Build AI-powered applications that store and retrieve domain-specific context from vector embeddings
- Deploy a containerized MCP server to provide remote LLM clients with vector search capabilities
- Create custom AI workflows where multiple agents share a centralized semantic knowledge base in Qdrant
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need MCP-compatible vector search integration.
The package is straightforward to deploy, has low install friction, carries a permissive license, and has no known vulnerabilities. The aging maintenance status (247 days since last release) is a minor caution but not a blocker for stable, read-write workloads. Verify active support if you plan to rely on ongoing updates.
Install
mcp-server-qdrant on PyPI
Before you install
Low install friction with a pure-Python wheel and four runtime dependencies. Maintenance status is aging—last release was 247 days ago—but the package remains functional and supports current Python versions.
Requires a running Qdrant instance (remote via QDRANT_URL or local via QDRANT_LOCAL_PATH) and Python >=3.10. fastembed will download its embedding model on first use.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions, making it suitable for most deployment contexts.
Quickstart
# Install
pip install mcp-server-qdrant
# Set environment variables
export QDRANT_URL="http://localhost:6333"
export COLLECTION_NAME="my-collection"
export EMBEDDING_MODEL="sentence-transformers/all-MiniLM-L6-v2"
# Run the server
from mcp_server_qdrant import app
# Server exposes qdrant-store and qdrant-find tools via MCPVerify before relying
- Whether the package is actively maintained or in maintenance-only mode beyond the aging signal
- Performance characteristics when handling large vector collections or high query volume
- Compatibility with specific Qdrant server versions or deployment modes
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesfastembedfastmcppydanticqdrant-client |
| Maintenance | Aging 247 days since the last release |
| First released | |
| Downloads | 1,334,257 / month, #4,038 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: mcp_server_qdrant-0.8.1-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “qdrant vector search mcp server”
- mcp-server-qdrantExposes Qdrant vector search as a Model Context Protocol server,…
- qdrant-clientPython client library for Qdrant vector search engine, supporting…
- mempalaceMemPalace stores conversation history and project files as verbatim…
Give your agent the search over MCP, or paste the wish link into any chat.
More Database packages
psycopg2-binary is a PostgreSQL database adapter for Python that implements the DB API 2.0 specification, enabling Python applications to connect to and query PostgreSQL databases with thread-safe concurrent operations.
Python client library for connecting to and executing commands against Redis key-value stores, supporting both synchronous and asynchronous operations.
Install it if your application needs to interact with Redis; the only prerequisite is a running Redis server instance.
YDB Python SDK is the official client library for connecting to and querying YDB databases from Python applications.
Install it if you need to connect Python applications to YDB databases.
Connects Python applications to Snowflake data warehouses using the DB API 2.0 specification, enabling SQL queries, data transfers, and warehouse operations.
sqlparse tokenizes SQL text into a tree of statements, clauses, and expressions, and provides functions to split scripts, format queries, and inspect parsed tokens without validating dialect or syntax.
Install it if you need to manipulate, format, or analyze SQL text programmatically.
Provides base adapter protocols and shared functionality that database adapters use to integrate with dbt-core, handling connections, dialect translation, relation caching, and core interface management.
See also wikipedia-mcp · qdrant-client · lean-lsp-mcp · mcp · llama-index-vector-stores-qdrant · chroma-mcp · jcodemunch-mcp · nextcloud-mcp-server · duckduckgo-mcp-server · excel-mcp-server