Qdrant MCP tool for ADK

Supported in ADKPythonTypeScript

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

=== "Python"

=== "Local MCP Server"

    ```python
    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-flash-latest",
        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,
                ),
            )
        ],
    )
    ```

=== "TypeScript"

=== "Local MCP Server"

    ```typescript
    import { LlmAgent, MCPToolset } from "@google/adk";

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

    const rootAgent = new LlmAgent({
        model: "gemini-flash-latest",
        name: "qdrant_agent",
        instruction: "Help users store and retrieve information using semantic search",
        tools: [
            new MCPToolset({
                type: "StdioConnectionParams",
                serverParams: {
                    command: "uvx",
                    args: ["mcp-server-qdrant"],
                    env: {
                        QDRANT_URL: QDRANT_URL,
                        COLLECTION_NAME: COLLECTION_NAME,
                        // QDRANT_API_KEY: QDRANT_API_KEY,
                    },
                },
            }),
        ],
    });

    export { rootAgent };
    ```

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.",
}

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