Wikidata MCP Server for Pydantic AIGive Pydantic AI instant access to 8 tools to Create Statement, Execute Sparql, Get Item, and more
GDPR Free for SubscribersPydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Wikidata through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.
Ask AI about this MCP Server for Pydantic AI
The Wikidata MCP Server for Pydantic AI is a standout in the The Unthinkable category — giving your AI agent 8 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
Geminiimport asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to Wikidata "
"(8 tools)."
),
)
result = await agent.run(
"What tools are available in Wikidata?"
)
print(result.data)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Wikidata MCP Server
Connect to Wikidata, the central storage for structured data of Wikimedia projects. This MCP server allows your AI agent to tap into millions of items, properties, and statements using both traditional SPARQL queries and modern vector-based semantic search.
Pydantic AI validates every Wikidata tool response against typed schemas, catching data inconsistencies at build time. Connect 8 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.
What you can do
- Entity Retrieval — Fetch full data and statements for any Wikidata Item (e.g., Q42) using the
get_itemandget_item_statementstools. - Advanced Querying — Execute complex SPARQL queries against the Wikidata Query Service (WDQS) with
execute_sparqlto find relationships and patterns across the entire graph. - Semantic Search — Use
search_items_vectorandsearch_properties_vectorto find entities and properties based on meaning rather than just exact keywords. - Data Contribution — Update the knowledge graph by creating statements or setting descriptions with
create_statementandset_item_description(requires OAuth). - Similarity Analysis — Compare text strings against specific entities to get semantic similarity scores using
get_similarity_score.
The Wikidata MCP Server exposes 8 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
All 8 Wikidata tools available for Pydantic AI
When Pydantic AI connects to Wikidata through Vinkius, your AI agent gets direct access to every tool listed below — spanning knowledge-graph, sparql, structured-data, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.
Create statement on Wikidata
Requires OAuth 2.0 Access Token. Create a new statement for an Item
Execute sparql on Wikidata
Use hint:Query hint:optimizer "None" if queries timeout. Execute a SPARQL query
Get item on Wikidata
g., Q42) via the Wikibase REST API. Retrieve a specific Wikidata Item
Get item statements on Wikidata
Retrieve statements for a Wikidata Item
Get similarity score on Wikidata
Compute similarity between text and an entity
Search items vector on Wikidata
Hybrid vector/keyword search for Items
Search properties vector on Wikidata
Hybrid vector/keyword search for Properties
Set item description on Wikidata
Requires OAuth 2.0 Access Token. Set an Item description
Connect Wikidata to Pydantic AI via MCP
Follow these steps to wire Wikidata into Pydantic AI. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.
Install Pydantic AI
pip install pydantic-aiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use Pydantic AI with the Wikidata MCP Server
Pydantic AI provides unique advantages when paired with Wikidata through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Wikidata integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Wikidata connection logic from agent behavior for testable, maintainable code
Wikidata + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Wikidata MCP Server delivers measurable value.
Type-safe data pipelines: query Wikidata with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Wikidata tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Wikidata and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Wikidata responses and write comprehensive agent tests
Example Prompts for Wikidata in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with Wikidata immediately.
"Search for Wikidata items related to 'artificial neural networks' using vector search."
"Run a SPARQL query to find the 5 most populated cities in Brazil."
"Get all statements for the Wikidata item Q42."
Troubleshooting Wikidata MCP Server with Pydantic AI
Common issues when connecting Wikidata to Pydantic AI through Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiWikidata + Pydantic AI FAQ
Common questions about integrating Wikidata MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.Does Pydantic AI validate MCP tool responses?
Can I switch LLM providers without changing MCP code?
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