LangChainFramework

Wikidata MCP Server

Bring Knowledge Graph
to LangChain

Name: Wikidata MCP Server
Price: 29 USD
Availability: InStock
Rating: 4.9 (1 reviews)
Author: Vinkius

Learn how to connect Wikidata to LangChain 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 Subscribers

Create StatementExecute SparqlGet ItemGet Item StatementsGet Similarity ScoreSearch Items VectorSearch Properties VectorSet Item Description

Unlock for AI Agents

Ask AI about this MCP Server ChatGPT Claude Perplexity

Compatible with every major AI agent and IDE

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

+ other MCP clients

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_item and get_item_statements tools.
Advanced Querying — Execute complex SPARQL queries against the Wikidata Query Service (WDQS) with execute_sparql to find relationships and patterns across the entire graph.
Semantic Search — Use search_items_vector and search_properties_vector to 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_statement and set_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)

create_statement

Requires OAuth 2.0 Access Token. Create a new statement for an Item

execute_sparql

Use hint:Query hint:optimizer "None" if queries timeout. Execute a SPARQL query

get_item

g., Q42) via the Wikibase REST API. Retrieve a specific Wikidata Item

get_item_statements

Retrieve statements for a Wikidata Item

get_similarity_score

Compute similarity between text and an entity

search_items_vector

Hybrid vector/keyword search for Items

search_properties_vector

Hybrid vector/keyword search for Properties

set_item_description

Requires OAuth 2.0 Access Token. Set an Item description

Why LangChain?

LangChain's ecosystem of 500+ components combines seamlessly with Wikidata through native MCP adapters. Connect 8 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

—
The largest ecosystem of integrations, chains, and agents. combine Wikidata MCP tools with 500+ LangChain components
—
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
—
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
—
Memory and conversation persistence let agents maintain context across Wikidata queries for multi-turn workflows

See it in action

Wikidata in LangChain

AI Agent→Vinkius

High Security·Kill Switch·Plug and Play

Why Vinkius

Wikidata and 4,000+ other MCP servers. One platform. One governance layer.

Teams that connect Wikidata to LangChain 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.

4,000+MCP Servers ready

<40msCold start

60%Token savings

	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

Unlock for AI Agents View step-by-step setup guide for LangChain →

Enterprise Security

Why teams choose Vinkius for Wikidata in LangChain

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 LangChain 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.

Unlock for AI Agents View full Wikidata details →

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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

The Vinkius Advantage

How Vinkius secures
Wikidata for LangChain

Every tool call from LangChain to the Wikidata MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.

< 40msCold start

Ed25519Signed audit chain

60%Token savings

FAQ

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 LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.