Connect your AI agent to CloudCrane over MCP: the workspace MCP (OAuth sign-in) and deployed tools.
- ✓Open-source license (MIT)
- ✓Actively maintained (<30d)
- ✓Clear description
- ✓Topics declared
- ✓Documented (README)
git clone https://github.com/cloudcrane-dev/cloudcrane-mcp{
"mcpServers": {
"cloudcrane-mcp": {
"command": "node",
"args": ["/path/to/cloudcrane-mcp/dist/index.js"]
}
}
}Resumen de MCP Servers
# CloudCrane MCP
[](https://smithery.ai/servers/cloudcrane/workspace)
[](https://glama.ai/mcp/connectors/ai.cloudcrane/workspace)
Connect your AI agent to [CloudCrane](https://cloudcrane.ai) over MCP.
**In Claude:** find [CloudCrane in the connectors directory](https://claude.ai/directory/cloudcrane) and connect.
CloudCrane turns a messy catalog into data an agent can be trusted with. Rules
run before any model, the model may only answer from values you allowed, and
every value carries a receipt saying how it was decided. Safety exclusions are
enforced in the database query, so a record whose value is unknown is left out
rather than assumed safe.
There are two MCP endpoints. Both speak **Streamable HTTP** (stateless, no SSE stream).
| Endpoint | Signs in with | What it opens |
|---|---|---|
| `https://cloudcrane.ai/api/build/mcp` | Your CloudCrane account (OAuth), or a build key `cc_build_…` | Your workspace, for your own agent while you build |
| `https://cloudcrane.ai/api/mcp/<tool>` | A tool key `cc_live_…` | One deployed tool, for your end users' agents |
## The workspace MCP
Let your own agent read what you are building and, if you allow it, help build it.
### Connect with OAuth
In an MCP client that supports OAuth sign-in, add the URL with no key:
```
https://cloudcrane.ai/api/build/mcp
```
The client opens a CloudCrane page where a workspace owner picks the workspace
and what the agent may do, then signs you in. Or install it from
[Smithery](https://smithery.ai/servers/cloudcrane/workspace).
Tested with **Claude**, **ChatGPT**, **Cursor**, **Cline** and **Smithery**. Exact settings for each are
in [`llms-install.md`](llms-install.md), which an agent can follow to set it up.
**Claude:** connect from the [connectors directory](https://claude.ai/directory/cloudcrane), or Settings → Connectors → Add custom connector with the URL above, keeping *Sign in now* and *Register automatically*.
**ChatGPT:** add a custom MCP server with the URL above and choose *OAuth*.
**Cursor** (`~/.cursor/mcp.json`):
```json
{ "mcpServers": { "cloudcrane": { "url": "https://cloudcrane.ai/api/build/mcp" } } }
```
**Cline** (`cline_mcp_settings.json`):
```json
{ "mcpServers": { "cloudcrane": { "transport": { "type": "streamableHttp", "url": "https://cloudcrane.ai/api/build/mcp" } } } }
```
The page shows where it will send you back before anything else, because an
app's name is only what it calls itself. It starts on read only. Each app you
approve shows up in the dashboard under **Developers**, where you can revoke it.
### Or with a build key
For a client without OAuth, an owner makes a build key under **Developers** and
sends it as a header:
```sh
claude mcp add --transport http cloudcrane https://cloudcrane.ai/api/build/mcp \
--header "Authorization: Bearer $CLOUDCRANE_BUILD_KEY"
```
### What it can do
**Every connection reads** (17 tools): `list_datasets`, `get_dataset`,
`list_contracts`, `get_readiness`, `list_review_items`, `get_receipts`,
`list_value_sets` and `get_run` to find its way around; `list_tools`,
`list_scenarios`, `get_tool_insights`, `list_drift_alerts`, `get_usage` and
`get_next_actions` to watch what is deployed and what needs doing;
`list_approvals`, `get_approval` and `list_events` to follow up. They run inside
a read-only database transaction.
**A connection allowed to build also gets 13:** `create_dataset`,
`create_field`, `update_field`, `create_value_set`, `import_value_set_version`,
`start_run`, `publish_release` and `request_approval` to build; `deploy_tool`,
`update_tool`, `create_tool_key`, `create_scenario` and `run_scenarios` to put it
in front of users. Each goes through the same checks as the dashboard, is
recorded as made by that connection, and is marked as changing data, so clients
such as Claude ask you before each call.
**What no connection can do:** publish past the accuracy gate, decide a review
item, edit a stored value, withhold a record, delete anything, or remove a value
from a safety field. Those stay with a person, because a receipt names who decided.
Imported record contents stay hidden unless the owner turns them on. The
workspace MCP is included in every CloudCrane plan, Free too.
Full reference: [cloudcrane.ai/docs/build-mcp](https://cloudcrane.ai/docs/build-mcp).
## A deployed tool
Each tool you deploy is its own MCP server. Your agent sees `search_<tool>` and
`get_<tool>`, plus `find_values` when the tool has value set fields. Their input
schema is generated from your contracts, so the agent picks values from an enum
of your list and cannot ask for one you never defined.
**Claude Code**
```sh
claude mcp add --transport http catalog https://cloudcrane.ai/api/mcp/catalog \
--header "Authorization: Bearer $CLOUDCRANE_TOOL_KEY"
```
**Claude Desktop, Cursor, Windsurf**
```json
{
"mcpServers": {
"catalog": {
"url": "https://cloudcrane.ai/api/mcp/catalog",
"headers": { "Authorization": "Bearer cc_live_..." }
}
}
}
```
Some clients call the block `servers` instead of `mcpServers`, and some want `"type": "http"` next to the url.
- **n8n:** MCP Client Tool node, transport *HTTP Streamable*, Bearer authentication.
- **LangChain:** `MultiServerMCPClient` with transport `streamable_http` and a headers dict.
- **OpenAI Agents SDK:** `MCPServerStreamableHttp` with the url and headers.
The same tool also answers plain REST at `POST /api/v1/tools/<tool>/search`.
Full reference: [cloudcrane.ai/docs/deploy](https://cloudcrane.ai/docs/deploy).
## Things that look like a broken server
- **406:** MCP requires `Accept: application/json, text/event-stream` on every POST, even though these endpoints never send a stream.
- **405 on GET:** the endpoints are stateless and offer no SSE stream, so only POST is allowed. A client that silently falls back to SSE connects but lists no tools.
- **403 on the workspace MCP:** it opens a whole workspace, so a request from a browser (any request with an `Origin` header) is refused. Call it from a server or a desktop client.
## Links
- [Docs](https://cloudcrane.ai/docs)
- [Playground](https://cloudcrane.ai/playground): the pipeline in your browser, no sign-up
- [Integrations](https://cloudcrane.ai/integrations)
This repository holds documentation and the registry entry (`server.json`), not
the server's source.
Lo que la gente pregunta sobre cloudcrane-mcp
¿Qué es cloudcrane-dev/cloudcrane-mcp?
+
cloudcrane-dev/cloudcrane-mcp es mcp servers para el ecosistema de Claude AI. Connect your AI agent to CloudCrane over MCP: the workspace MCP (OAuth sign-in) and deployed tools. Tiene 0 estrellas en GitHub y su última actualización registrada es del 2026-10-04.
¿Cómo se instala cloudcrane-mcp?
+
Puedes instalar cloudcrane-mcp clonando el repositorio (https://github.com/cloudcrane-dev/cloudcrane-mcp) o siguiendo las instrucciones del README en GitHub. ClaudeWave también te ofrece bloques de instalación rápida en esta misma página.
¿Es seguro usar cloudcrane-dev/cloudcrane-mcp?
+
Nuestro agente de seguridad ha analizado cloudcrane-dev/cloudcrane-mcp y le ha asignado un Trust Score de 95/100 (tier: Verified). Revisa el desglose completo de comprobaciones superadas y flags en esta página.
¿Quién mantiene cloudcrane-dev/cloudcrane-mcp?
+
cloudcrane-dev/cloudcrane-mcp es mantenido por cloudcrane-dev. La última actividad registrada en GitHub es del 2026-10-04, con 0 issues abiertos.
¿Hay alternativas a cloudcrane-mcp?
+
Sí. En ClaudeWave puedes explorar mcp servers similares en /categories/mcp, ordenados por popularidad o actividad reciente.
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