Context7
Up-to-date docs for AI agents
作者提供的说明(英文原文)
Search Artsdata for authoritative data on Canadian events, artists, organizations, and places.
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
适用于网页版、桌面版和手机版 Claude:
https://mcp.artsdata.ca/mcp在终端中运行这条命令
claude mcp add --transport http artsdata https://mcp.artsdata.ca/mcp粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"artsdata": {
"url": "https://mcp.artsdata.ca/mcp"
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"artsdata": {
"type": "http",
"url": "https://mcp.artsdata.ca/mcp"
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
ChatGPT 只能连接互联网上的服务器:
https://mcp.artsdata.ca/mcp菜单名称可能因套餐和版本不同而略有差异。
你的选择通过 Cookie 保存在这台设备上,无需账户。
这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
我们还没有读取这个服务器的工具列表;我们会轮流检查各个端点。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月6日
注册名称已通过域名 artsdata.ca 验证(注册中心会核实域名所有权)
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
尚未检查;我们会轮流检查各个端点
尚未检查;我们会轮流检查各个端点
尚未检查;我们会在检查端点时读取工具列表
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
它在作者的云端运行。通过互联网连接,无需安装,但你发送的内容会经过作者的服务器。
GitHub 仓库最后更新于–,最新版本(1.0.0)于2个月前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。它通过 URL 连接,所以接受远程 MCP 服务器的应用也可以使用,例如开启开发者模式的 ChatGPT。
同一分类中有:Context7, Codebase Memory, Exa
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
Up-to-date docs for AI agents
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Connect AI agents to Exa for web search, content fetching, and multi-step research.
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
4.0.68.1bundle install
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 agentsCalls to /mcp are reported to Google Analytics 4.
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/read asks the Artsdata Databus for the newest version of the artifact (GET /databus/artifact/latest?artifact= ) 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 from downloadUrl and compare version across reads to detect when a new dump lands.
.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/
ARTSDATA_RECONCILIATION_ENDPOINT: reconciliation service URL used by the search_entities, get_entity and search_events tools.ARTSDATA_SCHEMA_URL (optional): Turtle file compiled by the get_schema tool (default https://docs.artsdata.ca/artsdata-schema.ttl).ARTSDATA_SCHEMA_CACHE_TTL_SECONDS (optional): how long the compiled schema is cached in process (default 86400).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 (default https://api.artsdata.ca).ARTSDATA_SPARQL_ENDPOINT (optional): SPARQL endpoint queried by the sparql_query tool (default https://query.artsdata.ca/query).ARTSDATA_SPARQL_TIMEOUT_SECONDS (optional): read timeout for a sparql_query call (default 25).ARTSDATA_SPARQL_MAX_ROWS (optional): maximum rows sparql_query returns; extra rows are cut and the result is flagged truncated (default 1000).env/production.envenv/staging.envARTSDATA_INSTANCE_TYPE (optional): PRODUCTION (default) or STAGING. The Docker entrypoint loads the matching file from env/.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's G-XXXXXXXXXX id. Not a secret; defaulted for production in config/environments/production.rb.GA4_API_SECRET: Measurement Protocol secret. **Secret — set it as a Heroku. config var, never in env/*.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 answers 204 whether 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 to false to 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.
bundle exec rails server
The API will be available at http://localhost:3000.
Build the image:
docker build -t artsdata-mcp-server .
Run the server:
docker run --rm -p 3000:3000 artsdata-mcp-server
Run with staging profile:
docker run --rm -p 3000:3000 -e ARTSDATA_INSTANCE_TYPE=STAGING artsdata-mcp-server
Run tests in Docker:
docker run --rm artsdata-mcp-server bundle exec rails test
Run a single test file:
bundle exec rails test test/tool/search_events_test.rb
Run all tests:
bundle exec rails test
The public/llms.txt file is generated from MCP server metadata (tools, resources, descriptions, and schemas).
CI behavior:
Developer workflow:
bundle exec rake docs:generate_llms
bundle exec rake docs:check_llms
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.
The Artsdata MCP Server is actively deployed and hosted in a live production environment.
Live Production Endpoints