Wikidata MCP Server for LangChainGive LangChain instant access to 8 tools to Create Statement, Execute Sparql, Get Item, and more

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

GDPR Free for Subscribers

LangChain is the leading Python framework for composable LLM applications. Connect Wikidata through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

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The Wikidata MCP Server for LangChain is a standout in the The Unthinkable category — giving your AI agent 8 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

Vinkius delivers Streamable HTTP and SSE to any MCP client

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+ other MCP clients

python

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "wikidata": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Wikidata, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

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IAMAccess control

EU AI ActCompliant

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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

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.

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.

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.

The Wikidata MCP Server exposes 8 tools through the Vinkius. Connect it to LangChain 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 LangChain

When LangChain 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

Create statement on Wikidata

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

execute

Execute sparql on Wikidata

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

get

Get item on Wikidata

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

get

Get item statements on Wikidata

Retrieve statements for a Wikidata Item

get

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

Set item description on Wikidata

Requires OAuth 2.0 Access Token. Set an Item description

Connect Wikidata to LangChain via MCP

Follow these steps to wire Wikidata into LangChain. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 8 tools from Wikidata via MCP

Why Use LangChain with the Wikidata MCP Server

LangChain provides unique advantages when paired with Wikidata through the Model Context Protocol.

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

Wikidata + LangChain Use Cases

Practical scenarios where LangChain combined with the Wikidata MCP Server delivers measurable value.

RAG with live data: combine Wikidata tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Wikidata, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Wikidata tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Wikidata tool call, measure latency, and optimize your agent's performance

Example Prompts for Wikidata in LangChain

Ready-to-use prompts you can give your LangChain 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 LangChain

Common issues when connecting Wikidata to LangChain through Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Wikidata + LangChain FAQ

Common questions about integrating Wikidata MCP Server with LangChain.

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.

Can I trace MCP tool calls in LangSmith?

Yes. All MCP tool invocations appear as traced steps in LangSmith, showing input parameters, response payloads, latency, and token usage.

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