Mcp Wikidata

Name: Mcp Wikidata
Author: zzaebok

@zzaebok

Repository

About Mcp Wikidata

A server implementation for Wikidata API using the Model Context Protocol (MCP).

#mcp-wikidata #wikidata-api #model-context-protocol

Basic information

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "mcp-wikidata": {
      "command": "npx",
      "args": [
        "-y",
        "@smithery/cli",
        "install",
        "@zzaebok/mcp-wikidata",
        "--client",
        "claude"
      ]
    }
  }
}

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Mcp Wikidata?

Mcp Wikidata is a Model Context Protocol server that provides a set of tools for interacting with the Wikidata API. It allows AI agents to search for entity and property identifiers, retrieve metadata (label and description), and execute SPARQL queries against Wikidata.

How to use Mcp Wikidata?

Install via Smithery (npx -y @smithery/cli install @zzaebok/mcp-wikidata --client claude) or manually using uv after cloning the repository. Run the server with uv run src/server.py. The server exposes five tools that the LLM can invoke to answer queries about Wikidata entities and properties.

Key features of Mcp Wikidata

Search for a Wikidata entity ID by query string.
Search for a Wikidata property ID by query string.
Retrieve all properties associated with a given entity ID.
Execute arbitrary SPARQL queries on Wikidata.
Fetch the label and description for an entity in a specified language.

Use cases of Mcp Wikidata

Answer questions about people, places, or things by looking up their Wikidata entity.
Find the correct property ID for a relationship (e.g., “director” = P57).
List all properties of a known entity (e.g., get all statements for Bong Joon‑ho).
Run a SPARQL query to retrieve structured data (e.g., films directed by a person).
Combine entity lookup, property discovery, and SPARQL to perform multi‑step knowledge retrieval.