Wikidata MCP. Query global knowledge graphs from your AI agent.
Wikidata provides direct, structured access to the world's largest open knowledge graph. Run complex SPARQL queries, perform semantic vector searches for entities and properties, or fetch detailed facts about any item. Manage verifiable data relationships right from your agent.
Give Claude and any AI agent real-world access
Execute complex queries to map patterns and find specific links between entities across the entire knowledge graph.
Fetch all known statements, properties, and data points associated with a specific entity on Wikidata.
Find relevant entities or properties based on the underlying concept of your query, not just keyword matches.
Add new statements, descriptions, or relationships to Wikidata items (requires OAuth access).
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What AI agents can do with Wikidata with 8 Tools
These tools allow you to query, search, and even update data within the massive Wikidata knowledge graph directly from your AI client.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Wikidata MCPCreate Statement
Adds a new statement (fact) to an existing Wikidata item when provided with write permissions.
Execute Sparql
Runs sophisticated SPARQL queries to find relationships and patterns across the...
Get Item Statements
Retrieves every known property and statement linked to a specific Wikidata item ID.
Get Item
Pulls the core details and metadata for any specified Wikidata Item using its unique...
Get Similarity Score
Compares a piece of text against an entity to calculate how semantically similar...
Search Items Vector
Performs hybrid searches across items, finding matches based on both keywords and overall meaning.
Search Properties Vector
Searches for properties (types of facts) using a mix of keywords and semantic understanding.
Set Item Description
Updates the main textual description of an item on Wikidata when provided with write...
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Pulling facts from scattered sources feels like detective work.
Today, if you need to verify multiple facts about an entity—say, a person's life or a scientific concept—you end up clicking through Wikipedia, academic databases, and specialized wikis. You copy the date from one site, paste the occupation from another, and manually map out how they relate.
With this MCP, your agent handles the cross-referencing automatically. Instead of spending time on tabs and copy-pasting, you ask a single question, and the system executes complex queries to pull all verified statements in one go. You get structured data immediately.
Accessing Structured Knowledge with Wikidata
The tedious manual steps—copying an ID from a source, then running another search to find all its linked properties, and finally cross-referencing dates—all disappear. You simply ask your agent to retrieve the statements for the item.
What's different now is that you aren't just getting text; you're getting clean, structured data ready to be used in code or a database.
What Wikidata MCP does for your AI
You can treat Wikidata like a massive, interconnected database that speaks naturally with your AI client. Instead of asking your agent to guess at a fact based on general knowledge, you tell it to look up the source directly. You'll find everything from historical dates and scientific concepts to people's documented achievements.
The tool lets you run deep SPARQL queries across millions of linked items—the kind of complex relationship mapping that generic AI models just can't do reliably. It also supports modern vector search, so if you know what you mean but not the exact keywords, your agent finds it anyway. When you connect this MCP through Vinkius, you get to tap into all these capabilities from one place, making structured data retrieval a natural part of any workflow.
019e390b-29cf-732c-93ee-ba73ef2b84f5 How to set up Wikidata MCP
The bottom line is that your AI client gains a direct pipeline into highly structured, globally verifiable knowledge data.
Subscribe to the MCP and provide your User Agent identifier as required by Wikimedia policy.
If you need to write data back to Wikidata, connect an OAuth 2.0 Access Token.
Your agent can then execute complex queries or retrieve specific facts directly from this connection.
Who uses Wikidata MCP
This MCP serves researchers who need to verify facts, developers building systems on top of public data, and data scientists requiring structured datasets. If your job involves connecting disparate pieces of known information, you need this.
Verifies historical dates or scientific relationships by executing complex SPARQL queries against established records.
Extracts structured datasets about entities for analysis or model training without needing to download raw CSV files.
Finds entity IDs and property schemas, then uses the API to automatically enrich application data with external knowledge.
Benefits of connecting Wikidata MCP
Go beyond simple keyword matching. Use search_items_vector to find entities related by meaning, which is crucial when you're brainstorming or defining abstract concepts.
Map complex relationships instantly. With execute_sparql, you can ask the system to find patterns—like 'all famous writers who lived in London and wrote about space travel.'
Get all the verifiable facts on a subject. Running get_item_statements provides a complete breakdown of an item's known properties, letting you build comprehensive profiles.
Automate data enrichment by finding structured knowledge. Use get_item to pull core details for any entity, feeding clean data directly into your application logic.
Contribute verified information. If you have new facts about an item, use create_statement or set_item_description (with OAuth) to update the graph.
Wikidata MCP use cases
Fact-Checking Academic Research
A student needs to verify a claim about a historical figure's primary occupation. Instead of relying on generalized LLM knowledge, they ask their agent to run get_item and then use get_item_statements against the specific person's ID to pull only verifiable records.
Building Product Knowledge Bases
A developer wants to build a tool that connects related software concepts. They run multiple search_properties_vector queries to map out all associated technical terms and relationships, ensuring their internal data structure matches the global standard.
Complex Data Discovery
A journalist is researching economic trends across several continents. They use execute_sparql to write a query that finds patterns linking 'population growth' with 'infrastructure spending' in multiple regions simultaneously, something impossible with simple chat prompts.
Semantic Data Lookup
A marketing analyst needs data on 'early 20th-century photography techniques.' They use search_items_vector because the exact term might not be in the graph, but the semantic similarity engine guides them to the correct related items.
Wikidata MCP tradeoffs
What to watch out for, and the recommended way to handle each one.
Assuming general knowledge is sufficient
Asking your agent: 'What are the main properties of Q42?' The LLM will provide a summary, but it won't give you the underlying IDs or structured statements needed for coding.
Use get_item_statements on the item ID. This forces the system to pull all documented facts and their specific property links, giving you machine-readable data.
Handling complex cross-domain queries
Asking for 'the relationship between Roman coinage and modern industrial farming.' The LLM will hallucinate plausible connections because the query is too abstract.
Break it down. Use execute_sparql to first find all properties related to 'Roman coinage,' then use a second query on those results to see their documented links to other domains.
Trying to update data without permissions
Telling the agent: 'Set the description for Q192713.' The request fails silently or returns an error because no token was provided.
You must first connect your OAuth 2.0 Access Token, then run set_item_description to successfully modify the record.
When to use Wikidata MCP
Use this MCP if your task is inherently about verifiable facts, structured relationships, or deep data querying. If you need to build a system that requires knowing 'who is related to whom' in a documented way—whether it’s science, history, or technology—this is the tool. You should use execute_sparql when you know the exact pattern of data you want; use search_items_vector when you are browsing for concepts and need semantic help. Don't use this if your goal is creative text generation or summarization from unstructured documents, because while it can provide source material, its primary job is structured retrieval. If all you need is a general conversation about the topic, stick to your agent’s native chat capabilities; only bring in Wikidata when the answer must come from a highly controlled, global knowledge graph.
Frequently asked questions about Wikidata MCP
How do I use Wikidata with my agent for general fact retrieval? +
Use the get_item tool first. This fetches all core metadata and statements for any item ID, giving you a comprehensive overview of its known properties.
Can I run complex queries using execute_sparql in my chat client? +
Yes, execute_sparql allows your agent to run advanced queries against the entire knowledge graph. This is how you find patterns that simple searches miss.
What's the difference between searching items and searching properties with Wikidata? +
Use search_items_vector when you want to find an entity (a person, place, or thing). Use search_properties_vector when you are looking for a specific type of fact or relationship.
Is Wikidata MCP useful for updating data? +
Yes, if you have write access via OAuth, you can use tools like create_statement or set_item_description to add new facts or update existing descriptions.
Do I need a specific item ID to get all statements? +
You must provide the unique Wikidata Item ID (e.g., Q42) to use get_item_statements. This tells the system exactly which entity's data you want.