Wikidata MCP Server for LlamaIndexGive LlamaIndex 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

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Wikidata as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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

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python

import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Wikidata. "
            "You have 8 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Wikidata?"
    )
    print(response)

asyncio.run(main())

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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.

LlamaIndex agents combine Wikidata tool responses with indexed documents for comprehensive, grounded answers. Connect 8 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

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 LlamaIndex 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 LlamaIndex

When LlamaIndex 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 LlamaIndex via MCP

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

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 8 tools from Wikidata

Why Use LlamaIndex with the Wikidata MCP Server

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

Data-first architecture: LlamaIndex agents combine Wikidata tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Wikidata tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Wikidata, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Wikidata tools were called, what data was returned, and how it influenced the final answer

Wikidata + LlamaIndex Use Cases

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

Hybrid search: combine Wikidata real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Wikidata to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Wikidata for fresh data

Analytical workflows: chain Wikidata queries with LlamaIndex's data connectors to build multi-source analytical reports

Example Prompts for Wikidata in LlamaIndex

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

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Wikidata + LlamaIndex FAQ

Common questions about integrating Wikidata MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Wikidata tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.

Does LlamaIndex support async MCP calls?

Yes. LlamaIndex's async agent framework supports concurrent MCP tool calls for high-throughput data processing pipelines.

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