About Mcp Wikidata
An MCP server that wraps the Wikidata API. It provides tools for searching entity and property identifiers, reading metadata, and running SPARQL queries. It suits developers building LLM applications that need structured knowledge from Wikidata.
Key features
- 01Search Wikidata entity IDs
- 02Search Wikidata property IDs
- 03List properties for an entity
- 04Retrieve English label and description
- 05Execute SPARQL queries on Wikidata
Use cases
- 01Letting an LLM look up Wikidata identifiers
- 02Answering questions with SPARQL queries over Wikidata
- 03Recommending films based on Wikidata data
MCP support
The project is an MCP server that exposes the tools search_entity, search_property, get_properties, execute_sparql and get_metadata to MCP clients.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEWikidata MCP Server
A server implementation for Wikidata API using the Model Context Protocol (MCP). This project provides tools to interact with Wikidata, such as searching identifiers (entity and property), extracting metadata (label and description) and executing sparql query.
Installation
Installing via Smithery
To install Wikidata MCP Server for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @zzaebok/mcp-wikidata --client claude
Installing Manually
Install uv if it is not installed yet.
$ curl -LsSf https://astral.sh/uv/install.sh | sh
Then, install dependencies.
$ git clone https://github.com/zzaebok/mcp-wikidata.git
$ cd mcp-wikidata
$ uv sync
# if you want to run client example together
$ uv sync --extra example
Run
Run the server with:
$ uv run src/server.py
If you want to test it with a simple client code (with langchain-mcp-adapters), run the client with:
# in another shell
$ uv run src/client.py
The LLM extracts valid entity and property identifiers, executes a sparql query, and finally recommend a movie directed by Bong Joon-ho.
See the execution output
{
"messages": [
HumanMessage(
content="Can you recommend me a movie directed by Bong Joonho?",
),
AIMessage(
tool_calls=[
{
"name": "search_entity",
"args": {"query": "Bong Joon-ho"},
}
],
),
ToolMessage(
content="Q495980",
name="search_entity",
),
AIMessage(
tool_calls=[
{
"name": "get_properties",
"args": {"entity_id": "Q495980"},
}
],
),
ToolMessage(
content='["P345", "P244", "P214", "P227", ...]',
name="get_properties",
),
AIMessage(
tool_calls=[
{
"name": "search_property",
"args": {"query": "director"},
}
],
),
ToolMessage(
content="P57",
name="search_property",
),
AIMessage(
tool_calls=[
{
"name": "execute_sparql",
"args": {
"sparql_query": 'SELECT ?film ?filmLabel WHERE {\n ?film wdt:P57 wd:Q495980.\n SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }\n} LIMIT 1'
},
}
],
),
ToolMessage(
content='[{"film": {"type": "uri", "value": "http://www.wikidata.org/entity/Q483761"}, "filmLabel": {"xml:lang": "en", "type": "literal", "value": "Mother"}}]',
name="execute_sparql",
),
AIMessage(
content='I recommend the movie "Mother," which was directed by Bong Joon-ho.',
),
]
}
Wikidata MCP Tools
The following tools are implemented in the server:
| Tool | Description |
|---|---|
search_entity(query: str) | Search for a Wikidata entity ID by its query. |
search_property(query: str) | Search for a Wikidata property ID by its query. |
get_properties(entity_id: str) | Get the properties associated with a given Wikidata entity ID. |
execute_sparql(sparql_query: str) | Execute a SPARQL query on Wikidata. |
get_metadata(entity_id: str, language: str = "en") | Retrieve the English label and description for a given Wikidata entity ID. |
License
MIT License
Comments
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From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.