A Model Context Protocol (MCP) server that provides conversational interface for the exploration and analysis of RDF (Turtle) based Knowledge Graph
A Model Context Protocol (MCP) server that provides conversational interface for the exploration and analysis of RDF (Turtle) based Knowledge Graph in Local File
The server exposes the following resources: - schema://all: Retrieve schema information (classes and properties) from the graph. - Returns: A newline-separated string of schema elements (classes and properties).
Compare MCP servers by category, or browse hosted MCP servers if you prefer an endpoint over a local process.
Check the linked publisher documentation and your connected client's tool list. Names and availability can vary with the server version and account permissions.
| Tool | What it does |
|---|---|
| execute_on_endpoint | Execute a SPARQL query directly on an external endpoint |
| sparql_query | Execute a SPARQL query on the current graph or active external endpoint |
| graph_stats | Calculate and return statistics about the graph in JSON format |
| count_triples | Count triples in the graph. Disabled in SPARQL Endpoint Mode; use a custom prompt instead. |
| full_text_search | Perform a full-text search on the graph or endpoint, avoiding proprietary syntax. |
| health_check | Check the health of the triplestore connection. |
| get_mode | Get the current mode of RDF Explorer. Useful for knowledge graph and semantic tech users to verify data source. |
{
"mcpServers": {
"rdf_explorer": {
"command": "C:\\path\\to\\venv\\Scripts\\python.exe",
"args": ["C:\\path\\to\\server.py", "--triple-file", "your_file.ttl"]
}
}
}Configuration as documented by the project. Restart the client after saving.
Store entities, relations and observations in a local knowledge graph with the reference Memory MCP server.
A structured scratchpad for hard problems — stepwise reasoning with revisions, branches and visible logic.
<h1>CIE - Code Intelligence Engine</h1> <strong>20+ MCP tools that give AI agents semantic code search, call graph analysis, and endpoint discovery —
Swiss Truth is a **human + AI validated knowledge base** purpose-built for AI agents. Every fact passes a 5-stage validation pipeline, carries a
Local-first personal knowledge base. Cited answers from your vault via vector + graph retrieval.
Zero-dependency agent memory: recall, consolidation, and a first-class correction + erasure channel.