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

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Connect your CrewAI agents to Wikidata through Vinkius, pass the Edge URL in the `mcps` parameter and every Wikidata tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

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The Wikidata MCP Server for CrewAI 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
from crewai import Agent, Task, Crew

agent = Agent(
    role="Wikidata Specialist",
    goal="Help users interact with Wikidata effectively",
    backstory=(
        "You are an expert at leveraging Wikidata tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Wikidata "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 8 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)
Wikidata
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

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.

When paired with CrewAI, Wikidata becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Wikidata tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI

When CrewAI 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

Search items vector on Wikidata

Hybrid vector/keyword search for Items

search

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 CrewAI via MCP

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

01

Install CrewAI

Run pip install crewai
02

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com
03

Customize the agent

Adjust the role, goal, and backstory to fit your use case
04

Run the crew

Run python crew.py. CrewAI auto-discovers 8 tools from Wikidata

Why Use CrewAI with the Wikidata MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Wikidata through the Model Context Protocol.

01

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

02

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

03

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

04

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Wikidata + CrewAI Use Cases

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

01

Automated multi-step research: a reconnaissance agent queries Wikidata for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

02

Scheduled intelligence reports: set up a crew that periodically queries Wikidata, analyzes trends over time, and generates executive briefings in markdown or PDF format

03

Multi-source enrichment pipelines: chain Wikidata tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

04

Compliance and audit automation: a compliance agent queries Wikidata against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Example Prompts for Wikidata in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Wikidata immediately.

01

"Search for Wikidata items related to 'artificial neural networks' using vector search."

02

"Run a SPARQL query to find the 5 most populated cities in Brazil."

03

"Get all statements for the Wikidata item Q42."

Troubleshooting Wikidata MCP Server with CrewAI

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

01

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.
02

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".
03

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.
04

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Wikidata + CrewAI FAQ

Common questions about integrating Wikidata MCP Server with CrewAI.

01

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.
02

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.
03

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.
04

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.
05

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.

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