Read and build a CloudCrane workspace: datasets, field contracts, review queue, receipts, runs.
Model Context Protocol (MCP) Server: ai.cloudcrane/workspace
The ai.cloudcrane/workspace MCP server lets you read and build a CloudCrane workspace, covering workspace assets such as datasets, field contracts, review queue, receipts, and runs. It is described as a structured interface for interacting with workspace content.
🛠️ Key Features
Read a CloudCrane workspace
Build a CloudCrane workspace
Manage datasets
Manage field contracts
Access review queue
Access receipts
Access runs
🚀 Use Cases
Retrieve workspace datasets and related definitions
Update or create field contracts used by the workspace
Review items from the review queue
Use receipts and run records as part of workspace workflows
⚡ Developer Benefits
Workspace operations organized around datasets, contracts, review queue, receipts, and runs
Consistent access to workspace components through MCP
⚠️ Limitations
Only described functionality is workspace read/build across the listed components; additional tools, parameters, or coverage are not provided.
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:
code
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.
Tested with Claude, ChatGPT, Cursor, Cline and Smithery. Exact settings for each are
in llms-install.md, which an agent can follow to set it up.
Claude: connect from the connectors directory, 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.
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:
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.
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.
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.
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.
