@dcp/mcp
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., "@@dcp/mcpList available GPU types"
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.
@dcp/mcp — DCP Model Context Protocol server
DCP is Saudi Arabia's sovereign AI compute platform — in-Kingdom inference, GPU rental, and persistent storage, billed in SAR, on Saudi-owned hardware. This MCP server lets any MCP-capable agent (Claude Desktop, Claude Code, Cursor, or your own) use DCP through native tool calls.
It is a thin client: it reads DCP_API_KEY from the environment and calls https://api.dcp.sa. No secrets are bundled.
Install (one line)
The connector runs straight from GitHub — no npm install, no clone:
DCP_API_KEY=dc1-sk-xxxxxxxx npx -y github:dhnpmp-tech/dcp-mcpRelated MCP server: Run:AI MCP Server
MCP client config
Add this to your MCP client (Claude Desktop claude_desktop_config.json, Claude Code / Cursor .mcp.json):
{
"mcpServers": {
"dcp": {
"command": "npx",
"args": ["-y", "github:dhnpmp-tech/dcp-mcp"],
"env": { "DCP_API_KEY": "dc1-sk-xxxxxxxx" }
}
}
}Then ask your agent to "list DCP models" or "rent a GPU on DCP for 30 minutes." Agents with no key can call register_agent first — see the quickstart below.
Tools
Tool | What it does |
| Self-register in one call — get a real API key + small SAR trial, no human, no email click. Use first when you have no key. |
| List currently-serveable models (OpenAI-style). |
| Run a chat completion (OpenAI-compatible, in-Kingdom). |
| Renter wallet balance (SAR). |
| List rentable GPU types (e.g. |
| Rent a whole GPU as an interactive pod (root + Jupyter + SSH), prepaid per minute. |
| Pod status + access URL / SSH command / time remaining. |
| Add time to a running pod, no restart. |
| Stop early; unused prepaid time refunded. |
| Rent an exclusive in-Kingdom persistent volume (10/20/30 GB). |
| Active volume status + usage. |
Zero-human quickstart (for agents)
The whole loop runs with no human in it:
register_agent→ returns a realapi_key+ a 20 SAR trial credit (no email click). Set it asDCP_API_KEY.list_gpus→ pick agpu_typestring (e.g."H100","RTX 4090") from the live, available types.create_podwith thatgpu_type+duration_minutes→ pollget_podfor theaccess_url/ssh_commandonce running.chat→ run OpenAI-compatible inference on an available model fromlist_models.stop_pod→ stop early; unused prepaid minutes are refunded to the wallet.
Minting a key by hand (equivalent to register_agent):
curl -s -X POST https://api.dcp.sa/api/renters/agent-register \
-H 'Content-Type: application/json' -d '{}'
# → { "api_key": "dcp-renter-…", "trial_credit_sar": 20, "balance_sar": 20, ... }The trial (20 SAR) is enough to list GPUs, run a short pod, and do real inference; the larger grant stays behind email-verified signup. Calls are per-IP rate-limited.
Environment
DCP_API_KEY— renter API key (required for every tool exceptregister_agent).DCP_API_BASE— API host, defaulthttps://api.dcp.sa.
Why DCP
Inference, GPU rental, fine-tune hosting, and storage on Saudi-owned hardware inside the Kingdom — full PDPL / data-residency compliance, billed in SAR. The inference API is a drop-in OpenAI replacement: point any OpenAI SDK at https://api.dcp.sa/v1.
Learn more: https://dcp.sa/v2/agents · https://dcp.sa/llms.txt
MIT licensed.
This server cannot be deployed
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