How to Use the Flickr Photo Discovery MCP in Google ADK

Q: How do I register the Flickr Photo Discovery MCP server with the Google ADK?

Use the McpToolset class with the server's HTTP URL. Pass this toolset instance to your LlmAgent constructor to expose the search and metadata tools to Gemini.

Q: Can I limit which Flickr Photo Discovery tools are exposed to my Google ADK agent?

Yes. Use the optional toolnames filter when initializing your McpToolset to restrict access. For example, you can expose only searchflickrphotos and block metadata retrieval tools.

Q: Does Flickr Photo Discovery support BigQuery integration through the Google ADK?

Yes. Your agent can search for images using searchflickrphotos and write the returned metadata directly into BigQuery tables using standard Google Cloud libraries.

Connect Gemini to millions of public images using Flickr Photo Discovery and Google ADK for enterprise-scale visual reasoning.

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Connect Flickr Photo Discovery MCP to Google ADK

Create your Vinkius account to connect Flickr Photo Discovery to Google ADK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Flickr Photo Discovery with Google ADK

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Query public images via `search_flickr_photos` MCP Server

The `search_flickr_photos` tool executes text-based queries against the Flickr database to return public image records. By exposing this tool to the Google ADK, your Gemini model can search for specific visual subjects and process the results within its 1M+ token context window. This integration connects your cloud infrastructure directly to live photography feeds. You can feed the search results directly into BigQuery or use them to ground Gemini's reasoning with real-world imagery.

Analyze fresh uploads with `get_recent_flickr_photos`

The `get_recent_flickr_photos` tool fetches the newest public uploads from Flickr in real-time. By registering this toolset with your Google ADK agent, you enable automated ingestion pipelines that monitor public photography trends as they are published. The Gemini model processes these recent photo feeds to identify emerging visual patterns. Because the server runs on the Vinkius MCP platform, your enterprise agent avoids the overhead of managing raw HTTP connections or parsing complex API payloads.

Extract camera metadata using `get_flickr_photo_info`

The `get_flickr_photo_info` tool retrieves deep technical metadata, including aperture, focal length, and camera models, for any public photo ID. Your Google ADK agent uses this tool to audit image quality and technical specifications before archiving them. This detailed metadata allows Gemini to perform advanced photographic analysis. You can build agents that classify images based on camera hardware or exposure settings, storing the structured output directly in your Vertex AI databases.

Setup guide

Set up Flickr Photo Discovery MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Flickr Photo Discovery tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Flickr Photo Discovery_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Flickr Photo Discovery tools via MCP.",
    tools=mcp_tools,
)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Flickr. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Flickr Photo Discovery MCP in Google ADK

Use the `McpToolset` class with the server's HTTP URL. Pass this toolset instance to your `LlmAgent` constructor to expose the search and metadata tools to Gemini.

Yes. Use the optional `tool_names` filter when initializing your `McpToolset` to restrict access. For example, you can expose only `search_flickr_photos` and block metadata retrieval tools.

Yes. Your agent can search for images using `search_flickr_photos` and write the returned metadata directly into BigQuery tables using standard Google Cloud libraries.

The server supports both Stdio and HTTP transports. For hosted Google ADK agents, the HTTP transport is recommended to ensure reliable connections to the Vinkius MCP sandbox.

The server only touches public photo IDs, search parameters, and public metadata. All data transfers between the Google ADK agent and the Vinkius sandbox are protected by TLS encryption, ensuring your search terms remain private.

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