docs: Added Qdrant to third-party tools

docs/tools/index.md

@@ -263,6 +263,16 @@ Check out the following pre-built tools that you can use with ADK agents:
     </div>
   </a>
 
+  <a href="/adk-docs/tools/third-party/qdrant/" class="tool-card">
+    <div class="tool-card-image-wrapper">
+      <img src="../assets/tools-qdrant.png" alt="Qdrant">
+    </div>
+    <div class="tool-card-content">
+      <h3>Qdrant</h3>
+      <p>Store and retrieve information using semantic vector search</p>
+    </div>
+  </a>
+
   <a href="/adk-docs/tools/third-party/scrapegraphai/" class="tool-card">
     <div class="tool-card-image-wrapper">
       <img src="../assets/tools-scrapegraphai.png" alt="ScrapeGraphAI">

docs/tools/third-party/index.md

@@ -129,6 +129,16 @@ Check out the following third-party tools that you can use with ADK agents:
     </div>
   </a>
 
+  <a href="/adk-docs/tools/third-party/qdrant/" class="tool-card">
+    <div class="tool-card-image-wrapper">
+      <img src="../../assets/tools-qdrant.png" alt="Qdrant">
+    </div>
+    <div class="tool-card-content">
+      <h3>Qdrant</h3>
+      <p>Store and retrieve information using semantic vector search</p>
+    </div>
+  </a>
+
   <a href="/adk-docs/tools/third-party/scrapegraphai/" class="tool-card">
     <div class="tool-card-image-wrapper">
       <img src="../../assets/tools-scrapegraphai.png" alt="ScrapeGraphAI">

docs/tools/third-party/qdrant.md

@@ -0,0 +1,101 @@
+# Qdrant
+
+The [Qdrant MCP Server](https://github.com/qdrant/mcp-server-qdrant) connects
+your ADK agent to [Qdrant](https://qdrant.tech/), 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](https://cloud.qdrant.io/) (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"
+
+    ```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-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` (required)
+`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:
+
+```python
+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.",
+}
+```
+
+## Additional resources
+
+- [Qdrant MCP Server Repository](https://github.com/qdrant/mcp-server-qdrant)
+- [Qdrant Documentation](https://qdrant.tech/documentation/)
+- [Qdrant Cloud](https://cloud.qdrant.io/)

mkdocs.yml

@@ -226,6 +226,7 @@ nav:
         - Hugging Face: tools/third-party/hugging-face.md
         - Linear: tools/third-party/linear.md
         - Notion: tools/third-party/notion.md
+        - Qdrant: tools/third-party/qdrant.md
         - ScrapeGraphAI: tools/third-party/scrapegraphai.md
         - Tavily: tools/third-party/tavily.md
         - Agentic UI (AG-UI): tools/third-party/ag-ui.md