How to Use the Wikidata MCP in AutoGen

Q: Do agents need help running queries against Wikidata?

Yes. Agents can use the executesparql tool, but they'll also debate query hints (like optimizing for timeouts). This deliberation process ensures the SPARQL query is as robust and accurate as possible.

Build consensus-driven systems by having multiple agents debate Wikidata results with AutoGen.

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Connect Wikidata MCP to AutoGen

Create your Vinkius account to connect Wikidata to AutoGen 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 Wikidata with AutoGen

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Debating data structure changes

If an answer requires updating the knowledge graph, have your agents deliberate. They can argue over whether to use `create_statement` and what specific OAuth 2.0 Access Token is required for writing. This forces a consensus on structured data writes. One agent might flag security risks while another pushes for maximum detail when making a new fact.

Validating graph queries through debate

Use `execute_sparql` and let your agents challenge the query logic. Agent A runs the initial SPARQL, but Agent B can critique the results or suggest alternative query hints if it times out. The outcome is a verified conclusion on what the data actually says, preventing simple API calls from leading to flawed decisions.

Finding optimal search methods

When searching for an item, don't just call one tool. Have agents debate: should we run `search_items_vector` first? Or maybe check the properties with `search_properties_vector`? The multi-agent system converges on the most comprehensive search strategy by having competing viewpoints analyze the initial data.

Setup guide

Set up Wikidata MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Wikidata tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Wikidata_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Wikidata data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Wikidata_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Wikidata data")
    print(result.messages[-1].content)

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 Wikidata. 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 Wikidata MCP in AutoGen

Multiple agents debate which search function is best. They compare `search_items_vector` against `execute_sparql` to determine if a pure search or a precise query yields the most reliable information from Wikidata.

It manages statements and descriptions. Agents work together to decide whether the facts should be retrieved via `get_item_statements` or written back using `set_item_description`, ensuring consensus on the data structure.

Yes. Agents can use the `execute_sparql` tool, but they'll also debate query hints (like optimizing for timeouts). This deliberation process ensures the SPARQL query is as robust and accurate as possible.

Yes. By forcing multiple agents to review the output of tools like `get_item` or `get_similarity_score`, the system identifies potential ambiguities or conflicting facts in the raw data.

This server touches structured statements and item descriptions. When writing or updating, agents require an OAuth 2.0 Access Token to maintain secure control over the knowledge graph's data integrity.

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