Qdrant¶

The Qdrant MCP Server connects your ADK agent to Qdrant, an open-source vector search engine. This integration gives your agent the ability to store and retrieve information using semantic search.

Use cases¶

Semantic Memory for Agents: Store conversation context, facts, or learned information that agents can retrieve later using natural language queries.
Code Repository Search: Build a searchable index of code snippets, documentation, and implementation patterns that can be queried semantically.
Knowledge Base Retrieval: Create a retrieval-augmented generation (RAG) system by storing documents and retrieving relevant context for responses.

Prerequisites¶

A running Qdrant instance. You can:
- Use Qdrant Cloud (managed service)
- Run locally with Docker: docker run -p 6333:6333 qdrant/qdrant
(Optional) A Qdrant API key for authentication

Use with agent¶

Local MCP Server

from google.adk.agents import Agent
from google.adk.tools.mcp_tool import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import StdioConnectionParams
from mcp import StdioServerParameters

QDRANT_URL = "http://localhost:6333"  # Or your Qdrant Cloud URL
COLLECTION_NAME = "my_collection"
# QDRANT_API_KEY = "YOUR_QDRANT_API_KEY"

root_agent = Agent(
    model="gemini-2.5-pro",
    name="qdrant_agent",
    instruction="Help users store and retrieve information using semantic search",
    tools=[
        McpToolset(
            connection_params=StdioConnectionParams(
                server_params=StdioServerParameters(
                    command="uvx",
                    args=["mcp-server-qdrant"],
                    env={
                        "QDRANT_URL": QDRANT_URL,
                        "COLLECTION_NAME": COLLECTION_NAME,
                        # "QDRANT_API_KEY": QDRANT_API_KEY,
                    }
                ),
                timeout=30,
            ),
        )
    ],
)

Available tools¶

Tool	Description
`qdrant-store`	Store information in Qdrant with optional metadata
`qdrant-find`	Search for relevant information using natural language queries

Configuration¶

The Qdrant MCP server can be configured using environment variables:

Variable	Description	Default
`QDRANT_URL`	URL of the Qdrant server	`None` (required)
`QDRANT_API_KEY`	API key for Qdrant Cloud authentication	`None`
`COLLECTION_NAME`	Name of the collection to use	`None`
`QDRANT_LOCAL_PATH`	Path for local persistent storage (alternative to URL)	`None`
`EMBEDDING_MODEL`	Embedding model to use	`sentence-transformers/all-MiniLM-L6-v2`
`EMBEDDING_PROVIDER`	Provider for embeddings (`fastembed` or `ollama`)	`fastembed`
`TOOL_STORE_DESCRIPTION`	Custom description for the store tool	Default description
`TOOL_FIND_DESCRIPTION`	Custom description for the find tool	Default description

Custom tool descriptions¶

You can customize the tool descriptions to guide the agent's behavior:

env={
    "QDRANT_URL": "http://localhost:6333",
    "COLLECTION_NAME": "code-snippets",
    "TOOL_STORE_DESCRIPTION": "Store code snippets with descriptions. The 'information' parameter should contain a description of what the code does, while the actual code should be in 'metadata.code'.",
    "TOOL_FIND_DESCRIPTION": "Search for relevant code snippets using natural language. Describe the functionality you're looking for.",
}