How to Use the DBpedia MCP in Google ADK

Q: How do I connect the DBpedia MCP Server to my Google ADK agent?

Use pip install google-adk and configure McpToolset with your Vinkius HTTP endpoint. Pass the toolset into your LlmAgent constructor to give Gemini instant access to SPARQL and lookup tools.

Q: Can my Google ADK agent combine DBpedia data with BigQuery?

Yes, you can write agents that query DBpedia via querysparql and then join those results with your internal BigQuery tables. This is perfect for enriching customer data with public knowledge graphs.

Q: Can I restrict which DBpedia tools my Google ADK agent can access?

Yes, you can use the toolnames filter in the McpToolset configuration to limit access. For example, you can expose only lookupsearch if you want to prevent the agent from running expensive SPARQL queries.

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Connect DBpedia MCP to Google ADK

Create your Vinkius account to connect DBpedia 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 DBpedia with Google ADK

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Feed DBpedia knowledge into Google ADK pipelines

This DBpedia MCP Server lets Gemini-powered agents query structured Wikipedia facts directly inside your Google Cloud environment. By exposing `query_sparql` and `get_resource`, the server allows your enterprise agents to pull RDF data and match it against your existing BigQuery datasets. Because Gemini models support massive context windows, you can feed thousands of rows of retrieved Wikipedia data back into the prompt. The agent can then run complex reasoning tasks over entire DBpedia subgraphs in a single turn.

Real-time Wikipedia monitoring for Google ADK

The `get_live_changes` tool gives your Google Cloud agents a direct feed of current Wikipedia edits. Combined with `query_live_sparql`, your system can detect trending topics and pull the latest facts before they even hit search engines. Setting this up is straightforward with the `McpToolset` class, which connects your agent to the live HTTP stream. You can filter the tools using the `tool_names` parameter to only expose the live-sync capabilities to your monitoring agent.

Autocomplete Wikipedia lookups inside Google ADK

This DBpedia MCP Server uses `lookup_prefix` and `lookup_search` to turn raw user input into verified Wikipedia URIs. Instead of guessing entities, your agent queries DBpedia's index to get exact matches for people, places, or things. This prevents your Vertex AI models from hallucinating relationships or names. You get structured, verified data that fits perfectly into your downstream data pipelines.

Setup guide

Set up DBpedia 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 DBpedia 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="DBpedia_agent",
    model="gemini-2.0-flash",
    instruction="You have access to DBpedia 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 DBpedia. 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 DBpedia MCP in Google ADK

Use `pip install google-adk` and configure `McpToolset` with your Vinkius HTTP endpoint. Pass the toolset into your `LlmAgent` constructor to give Gemini instant access to SPARQL and lookup tools.

Yes, you can write agents that query DBpedia via `query_sparql` and then join those results with your internal BigQuery tables. This is perfect for enriching customer data with public knowledge graphs.

Yes, you can use the `tool_names` filter in the `McpToolset` configuration to limit access. For example, you can expose only `lookup_search` if you want to prevent the agent from running expensive SPARQL queries.

Gemini's million-token context window makes it perfect for processing large SPARQL payloads returned by `query_sparql`. It can analyze complex relationships in the data without running out of memory.

All queries run through an ephemeral Vinkius sandbox that handles transport. Your search strings and returned RDF payloads are processed in memory over a secure MCP connection and never logged or cached on our servers.

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