# WikidataMCP

> Model Context Protocol for Wikidata

- **Type:** MCP server
- **Install:** `agentstack add mcp-wmde-wikidatamcp`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [wmde](https://agentstack.voostack.com/s/wmde)
- **Installs:** 0
- **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml)
- **Latest version:** 0.1.0
- **License:** BSD-3-Clause
- **Upstream author:** [wmde](https://github.com/wmde)
- **Source:** https://github.com/wmde/WikidataMCP
- **Website:** https://wd-mcp.wmcloud.org/

## Install

```sh
agentstack add mcp-wmde-wikidatamcp
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Wikidata MCP
The **Wikidata MCP (Model Context Protocol)** provides a set of standardized tools that allow large language models (LLMs) to explore and query Wikidata programmatically. It is designed for agentic AI or AI workflows that need to search, inspect, and query Wikidata, without relying on hardcoded assumptions about its structure or content.

The Wikidata MCP server is running at [https://wd-mcp.wmcloud.org/](https://wd-mcp.wmcloud.org/) \
You can connect your AI application to it at [https://wd-mcp.wmcloud.org/mcp](https://wd-mcp.wmcloud.org/mcp) \
Tools are exposed as API endpoints and can be tested interactively at [https://wd-mcp.wmcloud.org/docs](https://wd-mcp.wmcloud.org/docs)

---

## 🧰 Tools
1. `search_items(query: str, lang: str = "en") -> str` \
Searches Wikidata items (QIDs) using vector search when available and falls back to keyword search when needed. Returns matching QIDs with labels and descriptions.

**Use When**: Starting exploration from a concept or natural-language description.

2. `search_properties(query: str, lang: str = "en") -> str` \
Searches Wikidata properties (PIDs) using vector search when available and falls back to keyword search when needed. Returns matching PIDs with labels and descriptions.

**Use When**: You need to find the right Wikidata property for relationships in statements or SPARQL.

3. `get_statements(entity_id: str, include_external_ids: bool = False, lang: str = "en") -> str` \
Returns direct statements (property-value pairs) for an entity in triplet-like text form. This tool excludes qualifiers, references, and deprecated values.

**Use When**: You want a fast structural overview of an entity.

4. `get_statement_values(entity_id: str, property_id: str, lang: str = "en") -> str` \
Returns all statement values for an entity-property pair, including qualifiers, references, and all ranks.

**Use When**: You need full statement detail for auditing, fact-checking, or provenance-sensitive tasks.

5. `get_instance_and_subclass_hierarchy(entity_id: str, max_depth: int = 5, lang: str = "en") -> str` \
Retrieves hierarchical context using "instance of" (P31) and "subclass of" (P279), returning JSON-formatted hierarchy data.

**Use When**: You need to understand entity classification before building filters in SPARQL.

6. `execute_sparql(sparql: str, K: int = 10) -> str` \
Executes a SPARQL query against Wikidata and returns up to `K` rows as CSV text.

**Use When**: You want structured retrieval and verification from Wikidata Query Service.

---

## 🚀 Running Locally
Run:

```bash
uv run python main.py
```

Then open:
- `http://localhost:8000/` for project page
- `http://localhost:8000/docs` for interactive Swagger UI
- `http://localhost:8000/mcp` for MCP clients

With Docker:

```bash
docker compose up --build
```

---

## 🌐 Services
### Vector Search

This service interfaces with the [Wikidata Vector Database](https://wd-vectordb.wmcloud.org/), enabling semantic search over Wikidata items using natural language. It is ideal for discovering relevant items without needing to know exact labels. This serves as a first step in exploratory or context-rich workflows.

🚀 API: [wd-vectordb.wmcloud.org](https://wd-vectordb.wmcloud.org/) \
📚 Docs: [wd-vectordb.wmcloud.org/docs](https://wd-vectordb.wmcloud.org/docs) \
📄 Project Page: [Wikidata Embedding Project](https://www.wikidata.org/wiki/Wikidata:Embedding_Project)

### Wikidata Textifier

This service returns readable triplet or textual representations of Wikidata entities, with resolved labels, optimized for use by language models.

🚀 API: [wd-textify.wmcloud.org](https://wd-textify.wmcloud.org/) \
📚 Docs: [wd-textify.wmcloud.org/docs](https://wd-textify.wmcloud.org/docs)

## Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [wmde](https://github.com/wmde)
- **Source:** [wmde/WikidataMCP](https://github.com/wmde/WikidataMCP)
- **License:** BSD-3-Clause
- **Homepage:** https://wd-mcp.wmcloud.org/

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/mcp-wmde-wikidatamcp
- Seller: https://agentstack.voostack.com/s/wmde
- Browse the marketplace: https://agentstack.voostack.com/browse

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Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.