Dcp Mcp

io.github.dhnpmp-techPublished by GitHub account dhnpmp-techv1.0.0Updated Oct 24, 2026

Saudi sovereign AI compute: OpenAI-compatible inference, rent GPU pods, manage storage.

VerifiedSTDIODesktop onlyCloud & Infrastructure Finance AI & ML

Server details

Identifier: io.github.dhnpmp-tech/dcp-mcp
Transport: STDIO
Runtime: Desktop only
Trust: Verified
Published: Sep 16, 2026

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Overview

AI-generated overview

A local MCP server that lets an assistant use DCP, a Saudi AI compute platform, for OpenAI-compatible chat inference, GPU pod rental and persistent storage.

What you can do: Register an agent and obtain an API key with a small trial credit, with no human or email step, and check the renter wallet balance in SAR. List available models and run OpenAI-compatible chat completions hosted in-Kingdom. List rentable GPU types with VRAM and live availability, then create, inspect, extend and stop prepaid GPU pods that expose root, Jupyter and SSH access. Rent and check in-Kingdom persistent volumes of 10, 20 or 30 GB.
When to use it: Use it when an assistant should run inference or rent GPU capacity on DCP's Saudi-hosted infrastructure, or when data-residency inside Saudi Arabia and SAR billing matter. It suits agents that need to provision a short-lived GPU pod or persistent volume through tool calls rather than a dashboard.
Requirements: Runs locally over stdio, launched with npx from a GitHub repository, so Node.js and network access to the DCP API are needed. The DCP_API_KEY environment variable is required for every tool except register_agent; DCP_API_BASE optionally overrides the default API host. Desktop MCP clients only.

Before you install

Most tools spend real money: create_pod, extend_pod and rent_volume are prepaid per minute or per volume, and stop_pod refunds only unused prepaid time. register_agent creates a real API key and trial credit without human verification, and calls are rate-limited per IP. The DCP_API_KEY is a billing credential; keep it out of shared configs. Pods grant root, Jupyter and SSH access, and prompts, data and workloads go to a third-party platform.

Installation

In SourceWeft

Open Dcp Mcp in the dashboard and add it to a workspace.
Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

@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:

MCP client config

Add this to your MCP client (Claude Desktop claude_desktop_config.json, Claude Code / Cursor .mcp.json):

json

{  "mcpServers": {    "dcp": {      "command": "npx",      "args": ["-y", "github:dhnpmp-tech/dcp-mcp"],      "env": { "DCP_API_KEY": "dc1-sk-xxxxxxxx" }    }  }}

{  "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
`register_agent`	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_models`	List currently-serveable models (OpenAI-style).
`chat`	Run a chat completion (OpenAI-compatible, in-Kingdom).
`get_balance`	Renter wallet balance (SAR).
`list_gpus`	List rentable GPU types (e.g. `H100`, `RTX 4090`) with VRAM + live availability.
`create_pod`	Rent a whole GPU as an interactive pod (root + Jupyter + SSH), prepaid per minute.
`get_pod`	Pod status + access URL / SSH command / time remaining.
`extend_pod`	Add time to a running pod, no restart.
`stop_pod`	Stop early; unused prepaid time refunded.
`rent_volume`	Rent an exclusive in-Kingdom persistent volume (10/20/30 GB).
`get_volume`	Active volume status + usage.

Zero-human quickstart (for agents)

The whole loop runs with no human in it:

register_agent → returns a real api_key + a 20 SAR trial credit (no email click). Set it as DCP_API_KEY.
list_gpus → pick a gpu_type string (e.g. "H100", "RTX 4090") from the live, available types.
create_pod with that gpu_type + duration_minutes → poll get_pod for the access_url / ssh_command once running.
chat → run OpenAI-compatible inference on an available model from list_models.
stop_pod → stop early; unused prepaid minutes are refunded to the wallet.

Minting a key by hand (equivalent to register_agent):

bash

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, ... }

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 except register_agent).
DCP_API_BASE — API host, default https://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.

Source: README.md at commit b9d485a

Tools

SourceWeft has not started this package in its sandbox yet, so its tools are not known.

Version history

v1.0.0LatestSep 16, 2026

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