Artsdata MCP Server
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., "@Artsdata MCP Serversearch Artsdata for Cirque du Soleil in French"
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.
Artsdata MCP Server (Rails)
Prerequisites
Ruby
4.0.6Rails
8.1Bundler
Related MCP server: RDF4J MCP Server
Setup
bundle installEndpoints
POST /mcp: MCP Streamable HTTP endpoint (tools and resources are documented in docs/mcp-server.md)GET /up: health checkGET /llms.txt: machine-readable guide for agents
Calls to /mcp are reported to Google Analytics 4.
MCP resources
artsdata://dumps/core-minus-provenance/latest(artsdata_dump): manifest for the latest Artsdata core-minus-provenance dump — the core graph with provenance and RDF-star annotations removed, published monthly as a gzipped Turtle file.resources/readasks the Artsdata Databus for the newest version of the artifact (GET /databus/artifact/latest?artifact=<ARTIFACT_URI>) and returns a small JSON document with the download URL, version, format and compression — never the dump contents. Going through the Databus means MCP clients need to know nothing about where the file is actually stored. Clients download the file fromdownloadUrland compareversionacross reads to detect when a new dump lands.
Directory structure
.github/
workflows/
deploy-to-heroku.yml # Workflow deploying to Heroku
run-unit-tests.yml # Reusable workflow running Minitest
unit-test-on-pull-request.yml # Workflow running unit tests on pull requests
app/
resource/ # MCP resources
schema/ # JSON schemas of the tools' input and output
services/ # Clients for the reconciliation service, SPARQL endpoint and Databus
tool/ # MCP tools
config/
initializers/mcp_server.rb # MCP server: registered tools, resources and transport
initializers/mcp_analytics.rb # Google Analytics 4 reporting configuration
routes.rb
lib/
mcp_analytics/ # Rack middleware reporting MCP usage to Google Analytics 4
test/Configuration
ARTSDATA_RECONCILIATION_ENDPOINT: reconciliation service URL used by thesearch_entities,get_entityandsearch_eventstools.ARTSDATA_SCHEMA_URL(optional): Turtle file compiled by theget_schematool (defaulthttps://docs.artsdata.ca/artsdata-schema.ttl).ARTSDATA_SCHEMA_CACHE_TTL_SECONDS(optional): how long the compiled schema is cached in process (default86400).ARTSDATA_CORS_ORIGINS(optional): comma-separated CORS origins (default*).ARTSDATA_API_ENDPOINT(optional): base URL of the Artsdata API, used to query the Databus for the latest data dump (defaulthttps://api.artsdata.ca).ARTSDATA_SPARQL_ENDPOINT(optional): SPARQL endpoint queried by thesparql_querytool (defaulthttps://query.artsdata.ca/query).ARTSDATA_SPARQL_TIMEOUT_SECONDS(optional): read timeout for asparql_querycall (default25).ARTSDATA_SPARQL_MAX_ROWS(optional): maximum rowssparql_queryreturns; extra rows are cut and the result is flaggedtruncated(default1000).Docker instance profiles are provided in:
env/production.envenv/staging.env
ARTSDATA_INSTANCE_TYPE(optional):PRODUCTION(default) orSTAGING. The Docker entrypoint loads the matching file fromenv/.
Usage analytics
Calls to /mcp are reported to Google Analytics 4, including the tool used,
how long the call took, and the caller's User-Agent. Reporting stays off until
both variables below are set, so development and CI need no configuration.
GA4_MEASUREMENT_ID: the GA4 property'sG-XXXXXXXXXXid. Not a secret; defaulted for production inconfig/environments/production.rb.GA4_API_SECRET: Measurement Protocol secret. Secret — set it as a Heroku. config var, never inenv/*.env, which is committed.GA4_DEBUG(optional): tag events so they appear in GA4's DebugView within seconds, and log whether GA4 considers each payload valid. Events are still recorded. Useful because the normal endpoint answers204whether or not the payload is valid.
Reporting is on when both credentials are present and off otherwise.
Separately, every MCP call is written to stdout as one JSON line, including the
complete arguments the agent sent — the full SPARQL query, search terms, entity
URIs. On Heroku these lines
go to heroku logs and to any drain you attach.
MCP_REQUEST_LOG(optional): set tofalseto silence the request log. It runs independently of Google Analytics and works with no GA4 credentials set.
For sparql_query calls, two extra facts are reported: sparql_form
(SELECT, ASK or UNKNOWN) and sparql_where, the
WHERE clause as the agent wrote it with the PREFIX block removed and whitespace
collapsed. Both go to Google Analytics and to the request log; GA4 cuts the
WHERE clause at its 100-character limit, the log keeps it whole.
Run the application
bundle exec rails serverThe API will be available at http://localhost:3000.
Docker
Build the image:
docker build -t artsdata-mcp-server .Run the server:
docker run --rm -p 3000:3000 artsdata-mcp-serverRun with staging profile:
docker run --rm -p 3000:3000 -e ARTSDATA_INSTANCE_TYPE=STAGING artsdata-mcp-serverRun tests in Docker:
docker run --rm artsdata-mcp-server bundle exec rails testRun tests
Run a single test file:
bundle exec rails test test/tool/search_events_test.rbRun all tests:
bundle exec rails testllms.txt maintenance
The public/llms.txt file is generated from MCP server metadata (tools, resources, descriptions, and schemas).
CI behavior:
Pull request CI runs bundle exec rake docs:check_llms.
If public/llms.txt is stale, the workflow fails.
Developer workflow:
Most of the time, no manual action is needed.
If you change MCP tools, resources, or schemas, regenerate the file with:
bundle exec rake docs:generate_llmsConfirm it is in sync with:
bundle exec rake docs:check_llmsCommit the updated public/llms.txt.
Deployment & CI/CD Details:
This project uses GitHub Actions for continuous integration and automated deployment to Heroku.
Every push to the main branch triggers a workflow that builds and deploys the application to Heroku.
The workflow is defined in .github/workflows/deploy-to-heroku.yml.
Every pull request triggers a workflow that unit tests. The workflow is defined in .github/workflows/run-unit-tests.yml.
Server Access & API Documentation
The Artsdata MCP Server is actively deployed and hosted in a live production environment.
Live Production Endpoints
MCP endpoint: https://mcp.artsdata.ca/mcp
Agent guide: https://mcp.artsdata.ca/llms.txt
This server cannot be deployed
Maintenance
Related MCP Connectors
Query, browse, and automate OmegaAI workspaces from any MCP client. Streamable HTTP with OAuth 2.0.
Governed data discovery, exact queries, decisions, simulations, and runtime utilities over MCP.
Search and fetch Wikidata entities, execute SPARQL queries, and resolve external identifiers.
OAuth-protected, read-only-by-default MCP server for provenance-labeled QuillCaddie project memory.
Related MCP Servers
- AlicenseNot gradedqualityAmaintenanceA Model Context Protocol server that provides tools for querying SPARQL endpoints, with specialized support for Proto-OKN knowledge graphs hosted on the FRINK platform.8BSD 3-Clause
- AlicenseAqualityDmaintenanceAn MCP server that enables AI-powered exploration of RDF data and SPARQL querying via RDF4J. It provides tools for executing queries, searching knowledge graph resources, and retrieving schema summaries.131MIT
- AlicenseAqualityCmaintenanceMCP server exposing SPARQL query functionalities for LLMs, enabling query execution, validation, and graph exploration across SPARQL endpoints.7MIT
- FlicenseNot gradedqualityCmaintenanceEnables MCP-compliant agents to perform data discovery, schema matching, and export via HTTP, with a mock mode for demonstration.-