mcp-dbpedia
Provides tools for querying DBpedia, a structured data source derived from Wikipedia, including lookup, SPARQL queries, resource fetching, and abstract retrieval.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@mcp-dbpediaTell me about Albert Einstein"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
@pipeworx/dbpedia
DBpedia MCP — structured knowledge extracted from Wikipedia. SPARQL endpoint + Lookup search. Keyless.
Part of Pipeworx — an MCP gateway connecting AI agents to 1476+ live data sources.
Tools
lookup(query, max_results?, type?)— DBpedia Lookup search (entity-resolution friendly)sparql(query, format?)— DBpedia SPARQL endpointresource(uri)— fetch all triples about a DBpedia resourceabstract(label, lang?)— fetch English/foreign abstract for a topic
Related MCP server: Wikidata MCP Server
Data source
Lookup:
https://lookup.dbpedia.org/api/SPARQL:
https://dbpedia.org/sparqlLinked Data:
https://dbpedia.org/resource/<Label>
Quick Start
Add to your MCP client (Claude Desktop, Cursor, Windsurf, etc.):
{
"mcpServers": {
"dbpedia": {
"url": "https://gateway.pipeworx.io/dbpedia/mcp"
}
}
}What this endpoint actually serves
tools/list at https://gateway.pipeworx.io/dbpedia/mcp returns the tools in the table
above plus the shared Pipeworx meta-tools — ask_pipeworx,
discover_tools, search_within, remember/recall and the rest of the
gateway-wide set. So the tool count you see is larger than this table: a
single-pack endpoint currently lists roughly 30 shared tools alongside the
pack's own. The connection's initialize response states its exact scope, and
is the authoritative answer for a given day.
This is deliberate, not multiplexing by accident. The meta-tools are what let a
scoped connection answer a question this pack does not cover — via
ask_pipeworx, which routes across the whole catalog — without you adding a
second MCP server. There is currently no way to mount a pack endpoint without
them; if the extra schemas cost you more context than the routing is worth,
connect to the full gateway once rather than to several pack endpoints.
Or connect to the full Pipeworx gateway to get every pack's tools listed directly, instead of just this one's:
{
"mcpServers": {
"pipeworx": {
"url": "https://gateway.pipeworx.io/mcp"
}
}
}Both URLs reach the same gateway and the same 1476+ data sources. The
only difference is which pack's tools are listed directly; ask_pipeworx
reaches all of them from either one.
Using with ask_pipeworx
Instead of calling tools directly, you can ask questions in plain English — this works on the pack endpoint above as well as on the full gateway:
ask_pipeworx({ question: "your question about Dbpedia data" })The gateway picks the right tool and fills the arguments automatically.
More
License
MIT
This server cannot be deployed
Maintenance
Related MCP Connectors
Search and fetch Wikidata entities, execute SPARQL queries, and resolve external identifiers.
Gateway between LLM agents and world data through eight tools and a bundled endpoint catalog.
Direct access to 60+ scraping and search tools. Extract structured data from Google (Search, Maps, Trends), Amazon, Airbnb, Social Media, and any web page directly into your AI agent.
Give your agent web search and authoritative datasets: S&P Global, FRED, OECD, SimilarWeb & more.
Related MCP Servers
- AlicenseBqualityDmaintenanceProvides access to Wikidata for Large Language Models through the Model Context Protocol, offering tools for entity search, detailed retrieval, SPARQL queries, relation exploration, and property-based searches.54MIT
- FlicenseNot gradedqualityDmaintenanceConnects LLMs to Wikidata's structured knowledge base using a hybrid architecture that optimizes for both fast entity searches and complex relational queries. It provides tools for entity and property retrieval, metadata lookups, and direct SPARQL execution to ground AI responses in verified data.2-
- AlicenseAqualityCmaintenanceEnables LLMs to search for keywords and fetch full page content from Wikipedia across various languages. It provides direct access to Wikipedia information through search and fetch tools.24MIT
- AlicenseNot gradedqualityFmaintenanceEnables AI agents to search, read, and explore Wikipedia articles via tools like summaries, categories, and random articles, with no API keys required.1MIT