squidex-mcp
An MCP server that lets AI agents read and write Squidex CMS content directly, with tools for schema listing, content querying, CRUD operations, and multiple profile support for different Squidex instances.
README
squidex-mcp (non-official)
An MCP (Model Context Protocol) server that lets AI agents read and write Squidex CMS content directly. MCP is an open, model-agnostic protocol — any compliant client works (Claude Code, Claude Desktop, Cursor, Windsurf, custom agents on any model), not just Anthropic's. Built on Bun for fast, dependency-free startup — each agent session spawns its own server process over stdio.
What it can do
| Tool | Purpose |
|---|---|
schema_list |
List content schemas in the app |
schema_get |
Get a schema's fields, including field ids and localization mode |
schema_create |
Create a new content schema |
schema_add_field |
Add a field to a schema (including nested Array/Component fields) |
schema_update_field |
Replace a field's properties |
schema_publish |
Publish a schema so content can be created against it |
schema_delete |
Delete a schema — for discarding a failed design iteration |
content_query |
Query content items (OData-style filter/top/skip/orderby/search) |
content_get |
Get a single content item |
content_create |
Create a content item |
content_update |
Replace a content item's data |
content_delete |
Delete a content item |
content_change_status |
Change workflow status (Draft/Published/Archived) |
asset_list |
List/query the asset library |
asset_get |
Get a single asset's metadata by id |
asset_upload |
Upload a new asset from a local file or a remote URL |
language_list |
List the app's configured languages, for partitioning localized fields |
profile_list |
List configured Squidex profiles (no secrets) |
It can target multiple Squidex apps/instances (e.g. prod + staging) from one running server — every tool takes an optional profile parameter, switchable without restarting the server. See Profiles below.
Quick start (no coding required)
The easiest way to run it is via npx — no download, no build step, just a Node.js install (which most machines already have).
1. Get your Squidex credentials
In your Squidex app: Settings → Clients → create (or copy) a client. You need:
- your Squidex URL (e.g.
https://cloud.squidex.io) - your app name
- the client ID (looks like
your-app-name:default) - the client secret
2. Register the server with your MCP client
Claude Code:
claude mcp add \
--env SQUIDEX_URL=https://cloud.squidex.io \
--env SQUIDEX_APP=your-app-name \
--env SQUIDEX_CLIENT_ID=your-app-name:default \
--env SQUIDEX_CLIENT_SECRET=your-client-secret \
--transport stdio squidex \
--scope user \
-- npx -y @shalotts/squidex-mcp
--scope user makes it available in every project, not just the current one. -y stops npx from pausing on an interactive "ok to install?" prompt, which would otherwise hang the connection on first run.
Claude Desktop: edit Claude Desktop's own config file for your OS (this is Claude Desktop's launcher config, not squidex.config.json from Profiles below — the env block here just sets environment variables for the process Claude Desktop spawns) —
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json ·
Windows: %APPDATA%\Claude\claude_desktop_config.json ·
Linux: ~/.config/Claude/claude_desktop_config.json
{
"mcpServers": {
"squidex": {
"command": "npx",
"args": ["-y", "@shalotts/squidex-mcp"],
"env": {
"SQUIDEX_URL": "https://cloud.squidex.io",
"SQUIDEX_APP": "your-app-name",
"SQUIDEX_CLIENT_ID": "your-app-name:default",
"SQUIDEX_CLIENT_SECRET": "your-client-secret"
}
}
}
}
Restart the client after editing.
3. Try it
Ask your assistant something like "List the content schemas in my Squidex app". If it replies with your schemas, it's working.
Alternative: standalone binary (no Node.js needed)
If Node.js/npx isn't available, download a prebuilt binary from the Releases page instead:
| OS | File |
|---|---|
| Linux (x64) | squidex-mcp-linux-x64 |
| Linux (ARM64) | squidex-mcp-linux-arm64 |
| macOS (Intel) | squidex-mcp-darwin-x64 |
| macOS (Apple Silicon) | squidex-mcp-darwin-arm64 |
| Windows (x64) | squidex-mcp-windows-x64.exe |
Make it executable on macOS/Linux (chmod +x ~/Downloads/squidex-mcp-<your-platform>; macOS may also require xattr -d com.apple.quarantine <file> since it isn't notarized), then use its absolute path as command instead of npx in the config above (and drop the args/-y bits, which are npx-specific).
Profiles (multiple Squidex instances)
If you need more than one Squidex app/instance (e.g. prod + staging) reachable from the same server, use a squidex.config.json file instead of env vars:
{
"defaultProfile": "prod",
"requestTimeoutMs": 15000,
"profiles": {
"prod": { "url": "https://cloud.squidex.io", "app": "blog", "clientId": "...", "clientSecret": "..." },
"staging": { "url": "https://cloud.squidex.io", "app": "blog-s", "clientId": "...", "clientSecret": "..." }
}
}
Every tool accepts an optional profile parameter to pick which target to use — no server restart needed to switch. The file is re-read on every call. requestTimeoutMs (default 15000) applies to every Squidex HTTP request across all profiles.
By default the server looks for squidex.config.json in its current working directory; point it elsewhere with the SQUIDEX_MCP_CONFIG env var (absolute path recommended, since the client process's working directory isn't always predictable). If no config file is found at all, the four SQUIDEX_* env vars from Quick start are used as a single implicit default profile.
Content field data must already be shaped per Squidex's partitioning ({ "title": { "iv": "..." } } for invariant fields, { "en": "...", "de": "..." } for localized ones) — call schema_get first to see each field's mode, and language_list to see which language codes are actually configured on the app.
Development
bun install
bun test # unit tests (config, token cache, query builder — no live Squidex needed)
bun run typecheck # tsc --noEmit
bun run dev # start the server with --watch, for local development
Point your MCP client at bun run src/index.ts in this directory instead of a downloaded binary while developing.
bun run build produces the Node-targeted dist/index.js that gets published to npm (dependencies stay external — installed normally via npm/npx, not bundled). Separately, bun run build:binary compiles a standalone binary for your current OS only (dist/squidex-mcp); bun run build:binary:all cross-compiles all five release targets at once (dist/squidex-mcp-<platform>) — the same command release.yml runs when a tag is pushed.
Local Squidex for end-to-end testing
docker-compose.yml runs a local Squidex + MongoDB for real (non-mocked) testing:
docker compose up -d # starts Squidex on http://localhost:8085
bun run scripts/e2e-bootstrap.ts # creates a "mcp-test" app + "posts" schema, writes squidex.config.json
bun run scripts/smoke.ts # or drive the tools directly via an MCP client
e2e-bootstrap.ts is idempotent — safe to re-run against an already-bootstrapped instance. It authenticates as the root superadmin client (created via IDENTITY__ADMINCLIENTID/IDENTITY__ADMINCLIENTSECRET in docker-compose.yml, dev-only credentials, not real secrets) and writes a local profile into squidex.config.json.
docker compose down stops it; add -v to also wipe the Mongo volume (fresh Squidex on next up).
Releasing
Pushing a tag matching v*.*.* triggers .github/workflows/release.yml: it runs the test suite, publishes the package to npm, cross-compiles standalone binaries for Linux/macOS/Windows, and attaches them to a GitHub Release.
git tag v0.2.0
git push origin v0.2.0
Publishing to npm uses Trusted Publishing (OIDC) — no token/secret needed. One-time setup: publish the first version manually (npm login && npm publish --access public, since Trusted Publisher configuration requires the package to already exist), then on the package's npmjs.com settings add a Trusted Publisher pointing at this repo (francyfox/squidex-mcp) and workflow file (release.yml). After that, every tag push publishes automatically.
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。