Bring Knowledge Graph
to Pydantic AI
Learn how to connect Wikidata to Pydantic AI and start using 8 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the 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.
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.
How it works
- Subscribe to this server
- Provide your User Agent (required by Wikimedia policy)
- Optionally provide an OAuth 2.0 Access Token for write operations
- Start exploring the world's knowledge from your favorite AI client
Who is this for?
- Researchers & Academics — instantly verify facts, dates, and relationships across history, science, and culture.
- Data Scientists — extract structured datasets for analysis or training without leaving the chat interface.
- Developers — find entity IDs and property schemas to integrate into applications or automate data enrichment.
Built-in capabilities (8)
Requires OAuth 2.0 Access Token. Create a new statement for an Item
Use hint:Query hint:optimizer "None" if queries timeout. Execute a SPARQL query
g., Q42) via the Wikibase REST API. Retrieve a specific Wikidata Item
Retrieve statements for a Wikidata Item
Compute similarity between text and an entity
Hybrid vector/keyword search for Items
Hybrid vector/keyword search for Properties
Requires OAuth 2.0 Access Token. Set an Item description
Why Pydantic AI?
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.
- —
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 in Pydantic AI
Wikidata and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect Wikidata to Pydantic AI through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.
Raw MCP | Vinkius | |
|---|---|---|
| Server catalog | Find and host yourself | 4,000+ managed |
| Infrastructure | Self-hosted | Sandboxed V8 isolates |
| Credential handling | Plaintext in config | Vault + runtime injection |
| Data loss prevention | None | Configurable DLP policies |
| Kill switch | None | Global instant shutdown |
| Financial circuit breakers | None | Per-server limits + alerts |
| Audit trail | None | Ed25519 signed logs |
| SIEM log streaming | None | Splunk, Datadog, Webhook |
| Honeytokens | None | Canary alerts on leak |
| Custom domains | Not applicable | DNS challenge verified |
| GDPR compliance | Manual effort | Automated purge + export |
Why teams choose Vinkius for Wikidata in Pydantic AI
The Wikidata 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. All 8 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in Pydantic AI only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.
* 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
How Vinkius secures
Wikidata for Pydantic AI
Every tool call from Pydantic AI to the Wikidata MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
How can I find a Wikidata Item if I don't know its Q-ID?
You can use the search_items_vector tool. It performs a hybrid search using high-dimensional embeddings and keywords to find the most relevant entities based on your natural language description.
Is it possible to run complex queries like 'List all female scientists born in the 19th century'?
Yes, the execute_sparql tool allows you to run any valid SPARQL query against the Wikidata Query Service. This is the most powerful way to filter and aggregate data across the entire knowledge graph.
Can I use this server to update information on Wikidata?
Yes, if you provide an OAuth 2.0 Access Token, you can use create_statement to add new data or set_item_description to update descriptions in various languages.
How does Pydantic AI discover MCP tools?
Create an 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?
Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.
Can I switch LLM providers without changing MCP code?
Absolutely. Pydantic AI abstracts the model layer. your Wikidata MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.
MCPServerHTTP not found
Update: pip install --upgrade pydantic-ai
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