A Model Context Protocol (MCP) server that retrieves information from Wikipedia to provide context to Large Language Models (LLMs). This tool helps
Wikipedia MCP MCP server is a locally run integration for AI assistants that speak the Model Context Protocol. A Model Context Protocol (MCP) server that retrieves information from Wikipedia to provide context to Large Language Models (LLMs). This tool helps AI assistants access factual information from Wikipedia to ground their responses in.
The Wikipedia MCP server provides real-time access to Wikipedia information through a standardized Model Context Protocol interface. This allows LLMs to retrieve accurate and up-to-date information directly from Wikipedia to enhance their responses.
The server ships on npm as @smithery/cli, so your MCP client can launch it on demand — there is no separate build step. Add the server block to your client's configuration, restart it, and the tools register themselves.
Once Wikipedia MCP is connected, these are the calls the assistant has available:
search_wikipedia — The search_wikipedia tool exposed by this serverget_article — The get_article tool exposed by this serverget_summary — The get_summary tool exposed by this serverget_sections — The get_sections tool exposed by this serverget_links — The get_links tool exposed by this serverget_coordinates — The get_coordinates tool exposed by this serverget_related_topics — Get topics related to a Wikipedia article based on links and categoriessummarize_article_for_query — Get a summary of a Wikipedia article tailored to a specific querysummarize_article_section — The summarize_article_section tool exposed by this serverextract_key_facts — Extract key facts from a Wikipedia article, optionally focused on a specific topic within the articleYou will need one environment variable: WIKIPEDIA_ACCESS_TOKEN. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
Plenty of developer tooling servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. Wikipedia MCP's toolset — search_wikipedia, get_article, get_summary and 7 more — is a fair guide to whether it matches your workflow. It is maintained by Rudra-ravi; worth a glance at recent repository activity before you build anything load-bearing on it.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
| Tool | What it does |
|---|---|
| search_wikipedia | The search_wikipedia tool exposed by this server. |
| get_article | The get_article tool exposed by this server. |
| get_summary | The get_summary tool exposed by this server. |
| get_sections | The get_sections tool exposed by this server. |
| get_links | The get_links tool exposed by this server. |
| get_coordinates | The get_coordinates tool exposed by this server. |
| get_related_topics | Get topics related to a Wikipedia article based on links and categories. |
| summarize_article_for_query | Get a summary of a Wikipedia article tailored to a specific query. |
| summarize_article_section | The summarize_article_section tool exposed by this server. |
| extract_key_facts | Extract key facts from a Wikipedia article, optionally focused on a specific topic within the article. |
{
"mcpServers": {
"wikipedia": {
"command": "npx",
"args": ["-y", "@smithery/cli"],
"env": {
"WIKIPEDIA_ACCESS_TOKEN": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
| Variable | Description | Required |
|---|---|---|
| WIKIPEDIA_ACCESS_TOKEN | Credential the server authenticates with. | Yes |
A knowledge graph your assistant keeps between sessions — entities, relations and observations that persist.
Kill hallucinated APIs — version-accurate, up-to-date library documentation injected straight into context.
Your workspace, on speaking terms with AI — search, read and write Notion pages and databases.
A structured scratchpad for hard problems — stepwise reasoning with revisions, branches and visible logic.
Symbol-level code navigation, refactoring and memory for coding agents — the IDE brain your assistant has been missing.
Chat with your second brain — search, read and write vault notes through the Local REST API.