Wikidata

Empower your agents to look up structured, factual knowledge about people, places, events, and concepts through Wikidata.

This guide will walk you through configuring the SVAHNAR tool and building knowledge retrieval workflows.

💡 Core Concepts

To configure this tool effectively, you need to understand what Wikidata is, what kinds of queries it answers well, and where its limits are.

1. What can this tool do?

The Wikidata tool queries the Wikidata knowledge base — a free, structured database of factual information maintained by the Wikimedia Foundation. It covers millions of entities: people, organizations, countries, scientific concepts, historical events, and more.

Capability	Description
Factual lookups	Answer direct factual questions about real-world entities.
Entity information	Retrieve structured data about people, places, organizations, and things.
Concept definitions	Look up what something is — scientific terms, historical events, works of art.
Relationship queries	Find relationships between entities — capitals, nationalities, founders, dates.

note

Wikidata contains structured, encyclopedic facts — it is not a search engine, news source, or opinion aggregator. It is best used for stable, verifiable facts about well-known entities. For current events or rapidly changing information, use web search tools instead.

2. Authentication

This tool uses the public Wikidata API — no authentication, API key, or credentials are required.

• No setup required: The Wikidata API is openly accessible. There is no token, OAuth flow, or developer account needed.
• Rate limits: The public API has generous rate limits sufficient for typical agent workloads. Avoid high-frequency automated bulk queries that could trigger throttling.

3. What Wikidata Answers Well

Wikidata excels at structured factual queries. Think of it as asking questions that have a single, verifiable answer:

Query Type	Example
Capital cities	"What is the capital of Japan?"
Biographical facts	"When was Marie Curie born?"
Country/geography	"What is the population of Germany?"
Scientific concepts	"What is the atomic number of carbon?"
Organizational info	"Who founded Apple Inc.?"
Historical dates	"When did World War II end?"
Works of art/literature	"Who wrote Don Quixote?"
Language & classification	"What language family does Hindi belong to?"

tip

Write queries as clear, direct questions or factual statements rather than keyword soup. "What is the capital of France?" returns better results than "France capital city".

4. Parameter Reference

Parameter	Type	Required	Description	Example
query	string	Yes	A natural language question or keyword string about any real-world entity or concept.	"What is the capital of France?"

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⚙️ Configuration Steps

Add the Tool in SVAHNAR

1. Open your SVAHNAR Agent Configuration.
2. Add the Wikidata tool.
3. No credentials are required — the tool connects to the public Wikidata API automatically.
4. Save the configuration.

Verify the Connection

To confirm the tool is working:

1. Trigger a test agent run with a simple factual query:

{
  "query": "What is the capital of France?"
}

2. A valid response will return Paris along with structured entity data.
3. If the tool returns an empty response, try rephrasing the query as a clearer, more direct question.

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📚 Practical Recipes (Examples)

Recipe 1: Factual Q&A Agent

Use Case: An agent that answers direct factual questions by grounding its responses in Wikidata's structured knowledge base rather than model memory.

create_vertical_agent_network:
  agent-1:
    agent_name: factual_qa_agent
    LLM_config:
        params:
          model: gpt-4o
    tools:
      tool_assigned:
        - name: Wikidata
    agent_function:
      - You are a factual knowledge assistant.
      - For any question about a real-world entity, person, place, event, or concept — use Wikidata to look up the answer rather than relying on your own knowledge.
      - Pass the user's question directly as the 'query' field.
      - Ground your response in the Wikidata result and clearly state the source of the information.
      - If Wikidata returns no result, acknowledge the gap and suggest the user try Wikipedia or a web search for less-structured topics.
    incoming_edge:
      - Start
    outgoing_edge: []

---

Recipe 2: Entity Enrichment Agent

Use Case: An agent that enriches a list of names, places, or organizations with verified structured data from Wikidata.

create_vertical_agent_network:
  agent-1:
    agent_name: entity_enrichment_agent
    LLM_config:
        params:
          model: gpt-4o
    tools:
      tool_assigned:
        - name: Wikidata
    agent_function:
      - You are an entity enrichment assistant.
      - For each item in the user's list (people, companies, countries, etc.), run a separate Wikidata query using the entity name as 'query'.
      - Extract key structured facts returned — founding date, headquarters, nationality, notable works, population, or whatever is relevant to the entity type.
      - Return a structured enrichment table with one row per entity and the retrieved facts as columns.
    incoming_edge:
      - Start
    outgoing_edge: []

---

Recipe 3: Cross-Tool — Wikidata + Reddit Research Agent

Use Case: An agent that first grounds itself in verified facts from Wikidata, then searches Reddit for community discussion on the same topic.

create_vertical_agent_network:
  agent-1:
    agent_name: grounded_research_agent
    LLM_config:
        params:
          model: gpt-4o
    tools:
      tool_assigned:
        - name: Wikidata
        - name: Reddit
          config:
            client_id: ${reddit_client_id}
            client_secret: ${reddit_client_secret}
            user_agent: ${reddit_user_agent}
    agent_function:
      - You are a grounded research assistant.
      - When the user asks about a topic, first use Wikidata to retrieve verified factual background — what it is, key dates, key figures, and established facts.
      - Then use Reddit to search for community discussion and real-world opinions on the same topic in a relevant subreddit.
      - Present findings in two sections — 'Verified Facts' (from Wikidata) and 'Community Perspective' (from Reddit) — so the user can clearly distinguish established knowledge from opinion.
    incoming_edge:
      - Start
    outgoing_edge: []

💡 Tip: When to Use Wikidata vs Web Search

Use Wikidata when...	Use web search when...
The fact is stable and encyclopedic	The information changes frequently
You need structured entity data	You need current news or events
The entity is well-known globally	The topic is niche or regional
You want a verifiable, sourced answer	You need opinion, analysis, or commentary

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🚑 Troubleshooting

• Empty or No Results Returned

  • Rephrase the query as a clear, direct question — e.g., "Who founded Tesla?" instead of "Tesla founder".
  • Wikidata works best for globally well-known entities. Very niche, local, or newly created entities may not have Wikidata entries yet.
  • If the entity exists but is known by multiple names, try the most commonly used English name or the official name.

• Results Are About the Wrong Entity

  • Wikidata may resolve an ambiguous name to the most prominent entity with that name. Be more specific in the query — e.g., "Paris, France" instead of just "Paris" to avoid getting the Paris Hilton entry.
  • Include disambiguating context in the query — profession, country, or domain — to improve precision.

• Factual Answer Seems Outdated

  • Wikidata is community-maintained and may lag behind recent changes — leadership changes, population updates, or newly renamed entities may not be current.
  • For time-sensitive facts, verify with a web search tool after retrieving the Wikidata baseline.

• Tool Returns Data But the Answer Is Incomplete

  • Wikidata's coverage is uneven — major world entities are richly documented, while smaller or regional ones may have sparse entries.
  • For richer narrative context, complement Wikidata lookups with a Wikipedia search or web search on the same topic.