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mcp-server-sparql

by Kashif-Rabbani
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Python
Remote

mcp-server-sparql

A generic Model Context Protocol (MCP) server that connects AI agents to any SPARQL 1.1 endpoint.

Works with Apache Jena Fuseki · GraphDB · Stardog · Virtuoso · Amazon Neptune · Blazegraph — or any endpoint that speaks SPARQL 1.1.

Tools

Tool

Description

sparql_select

Execute a SELECT query → tabular rows; paginate with limit/offset/has_more

sparql_ask

Execute an ASK query → boolean; fast existence/reachability checks

sparql_construct

Execute a CONSTRUCT query → RDF graph string

sparql_describe

DESCRIBE a resource URI → all triples about it

sparql_update

Any SPARQL 1.1 Update: INSERT DATA, DELETE, CLEAR, DROP, LOAD, COPY, MOVE, ADD

graph_list

List all named graphs with triple counts

graph_get

Fetch a named graph as Turtle; paginated

graph_put

Replace a named graph (inline content or remote URL via server-side LOAD)

graph_post

Append to a named graph

graph_delete

Delete a named graph

validate_shacl

Validate RDF against SHACL shapes — pure function, no endpoint needed

endpoint_ping

Check connectivity; returns response time and configured URLs

Connection is configured once at the server level via environment variables. Tools take no connection parameters — the same pattern used by mcp-server-postgres and mcp-server-sqlite.

Related MCP server: Ontology MCP

Quickstart

git clone https://github.com/Kashif-Rabbani/mcp-server-sparql.git
cd mcp-server-sparql
pip install -e .

cp .env.example .env
# edit .env — set SPARQL_ENDPOINT (or the three explicit URL vars)

python server.py

Server starts on http://0.0.0.0:8000. The SSE endpoint is at /sse.

Configuration

Copy .env.example to .env and set one of:

Option A — single base URL (Fuseki / Stardog):

SPARQL_ENDPOINT=http://localhost:3030/myDataset

This auto-derives /query, /update, and /data suffixes.

Option B — explicit URLs (GraphDB, Virtuoso, Neptune, etc.):

SPARQL_QUERY_URL=http://localhost:7200/repositories/myrepo
SPARQL_UPDATE_URL=http://localhost:7200/repositories/myrepo/statements
SPARQL_GRAPH_STORE_URL=http://localhost:7200/repositories/myrepo/rdf-graphs/service

Auth (optional):

SPARQL_USERNAME=admin
SPARQL_PASSWORD=secret

Endpoint-specific notes

Endpoint

Option

Query URL pattern

Apache Jena Fuseki

A

http://host:3030/<dataset>

Stardog

A

http://host:5820/<database>

GraphDB

B

http://host:7200/repositories/<repo>

Amazon Neptune

B

https://<cluster>.neptune.amazonaws.com:8182/sparql

Virtuoso

B

http://host:8890/sparql

Blazegraph

B

http://host:9999/blazegraph/namespace/<ns>/sparql

Connect to an MCP client

Claude Desktop (claude_desktop_config.json)

{
  "mcpServers": {
    "sparql": {
      "command": "uvicorn",
      "args": ["server:app", "--host", "0.0.0.0", "--port", "8000"],
      "cwd": "/path/to/mcp-server-sparql",
      "env": {
        "SPARQL_ENDPOINT": "http://localhost:3030/myDataset"
      }
    }
  }
}

Vectara Agent Platform

Register the server as an MCP Tool Server pointing at the public SSE URL (use ngrok for local development):

ngrok http 8000
# then register https://<id>.ngrok-free.app/sse as an MCP tool server in Vectara

Requirements

  • Python 3.11+

  • A running SPARQL 1.1 endpoint

License

MIT

This server cannot be deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Resources

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