Terradev MCP Server v2.0.5

Complete Agentic GPU Infrastructure for Claude Code — 192 MCP tools: GPU provisioning, vLLM/SGLang/Ollama inference, Arize Phoenix observability, NeMo Guardrails safety, Qdrant vector DB, Ray cluster management, Datadog monitoring, and Terraform-powered parallel provisioning across 20 cloud providers.

What's New in v2.0.5

• 192 MCP Tools: Massively expanded from 69 → 192 tools
• Arize Phoenix: LLM trace observability — projects, spans, traces, OTEL env, K8s deployment (7 tools)
• NeMo Guardrails: Output safety — test, chat, config generation, K8s deployment (4 tools)
• Qdrant Vector DB: RAG infrastructure — collections, create, info, count, K8s deployment (6 tools)
• Datadog Monitoring: Metrics, monitors, dashboards, Terraform export (10 tools)
• HuggingFace Hub: Models, datasets, endpoints, smart templates, hardware recommendations (11 tools)
• LangChain/LangGraph: Workflow creation, orchestrator-worker, evaluation (9 tools)
• 20 Cloud Providers: Alibaba Cloud, OVHcloud, FluidStack, Hetzner, SiliconFlow + 15 more
• vLLM Cost Optimizations: LMCache, KV Cache Offloading, MTP Speculative Decoding, Sleep Mode, Multi-LoRA
• Data Governance: Consent management, OPA policy evaluation, compliance reports (6 tools)
• Cost Intelligence: Deep analysis, simulation, budget optimization (4 tools)

Previous Releases

• Claude.ai Connector: OAuth 2.0 PKCE flow for remote access
• MoE Cluster Templates: Production-ready infrastructure for Mixture-of-Experts models
• NVLink Topology Enforcement: Automatic single-node TP with NUMA-aligned GPU placement
• Terraform Core Engine: All GPU provisioning uses Terraform for optimal parallel efficiency

Architecture

Terraform is the fundamental engine - not just a feature. This provides:

• ✅ True Parallel Provisioning across multiple providers simultaneously
• ✅ State Management for infrastructure tracking
• ✅ Infrastructure as Code with reproducible deployments
• ✅ Cost Optimization through provider arbitrage
• ✅ Bug-Free Operation with all known issues resolved

Installation

Prerequisites

1. Install Terradev CLI (v3.7.0+):

pip install terradev-cli
# For all providers + HF Spaces:
pip install "terradev-cli[all]"

2. Set up minimum credentials (RunPod only):

export TERRADEV_RUNPOD_KEY=your_runpod_api_key

3. Install the MCP server:

npm install -g terradev-mcp

Claude Code Setup (Local — stdio)

Add to your Claude Code MCP configuration:

{
  "mcpServers": {
    "terradev": {
      "command": "terradev-mcp"
    }
  }
}

Claude.ai Connector Setup (Remote — SSE)

Use Terradev from Claude.ai on any device — no local install required.

1. Go to Claude.ai → Settings → Connectors
2. Add a new connector with URL:
   code

   https://terradev-mcp.terradev.cloud/sse
   

3. Enter the Bearer token provided by your admin.

That's it — GPU provisioning tools are now available in every Claude.ai conversation.

Self-Hosting the Remote Server

To host your own instance:

# Set required env vars
export TERRADEV_MCP_BEARER_TOKEN=your-secret-token
export TERRADEV_RUNPOD_KEY=your-runpod-key

# Option 1: Run directly
pip install -r requirements.txt
python3 terradev_mcp.py --transport sse --port 8080

# Option 2: Docker
docker-compose up -d

The server exposes:

• GET /sse — SSE stream endpoint (Claude.ai connects here)
• POST /messages — MCP message endpoint
• GET /health — Health check (unauthenticated)

See nginx-mcp.conf for reverse proxy configuration with SSL.

Available MCP Tools

The Terradev MCP server provides 192 tools for complete GPU cloud management:

GPU Operations

• local_scan - Discover local GPU devices and total VRAM pool (NEW in v1.2.2)
• quote_gpu - Get real-time GPU prices across all cloud providers
• provision_gpu - Terraform-powered GPU provisioning with parallel efficiency

Terraform Infrastructure Management

• terraform_plan - Generate Terraform execution plans
• terraform_apply - Apply Terraform configurations
• terraform_destroy - Destroy Terraform-managed infrastructure

Kubernetes Management

• k8s_create - Create Kubernetes clusters with GPU nodes
• k8s_list - List all Kubernetes clusters
• k8s_info - Get detailed cluster information
• k8s_destroy - Destroy Kubernetes clusters

Inference & Model Deployment

• inferx_deploy - Deploy models to InferX serverless platform
• inferx_status - Check inference endpoint status
• inferx_list - List deployed inference models
• inferx_optimize - Get cost analysis for inference endpoints
• hf_space_deploy - Deploy models to HuggingFace Spaces

MoE Expert Parallelism (NEW in v1.5)

• deploy_wide_ep - Deploy MoE model with Wide-EP across multiple GPUs via Ray Serve LLM
• deploy_pd - Deploy disaggregated Prefill/Decode serving with NIXL KV transfer
• ep_group_status - Health check EP groups (all ranks must be healthy for all-to-all)
• sglang_start - Start SGLang server with EP/EPLB/DBO flags via SSH/systemd
• sglang_stop - Stop SGLang server on remote instance

Instance & Cost Management

• status - View all instances and costs
• manage_instance - Stop/start/terminate GPU instances
• analytics - Get cost analytics and spending trends
• optimize - Find cheaper alternatives for running instances

Provider Configuration

• setup_provider - Get setup instructions for any cloud provider
• configure_provider - Configure provider credentials locally

Arize Phoenix — LLM Trace Observability

• phoenix_test - Test connection to Phoenix server
• phoenix_projects - List Phoenix projects
• phoenix_spans - List spans with SpanQuery DSL filters
• phoenix_trace - View full execution tree for a trace
• phoenix_otel_env - Generate OTEL env vars for serving pods
• phoenix_snippet - Generate Python instrumentation snippet
• phoenix_k8s - Generate K8s deployment manifest

NeMo Guardrails — Output Safety

• guardrails_test - Test connection to Guardrails server
• guardrails_chat - Send message through safety rails
• guardrails_generate_config - Generate Colang 2.x config
• guardrails_k8s - Generate K8s deployment manifest

Qdrant — Vector Database for RAG

• qdrant_test - Test connection to Qdrant
• qdrant_collections - List vector collections
• qdrant_create_collection - Create collection (auto-configures from embedding model)
• qdrant_info - Get collection stats
• qdrant_count - Count vectors in collection
• qdrant_k8s - Generate K8s StatefulSet manifest

Complete Command Reference

Local GPU Discovery (NEW!)

# Scan for local GPUs
terradev local scan

# Example output:
# ✅ Found 2 local GPU(s)
# 📊 Total VRAM Pool: 48 GB
#
# Devices:
# • NVIDIA GeForce RTX 4090
#   - Type: CUDA
#   - VRAM: 24 GB
#   - Compute: 8.9
#
# • Apple Metal
#   - Type: MPS
#   - VRAM: 24 GB
#   - Platform: arm64

Hybrid Use Case: Mac Mini (24GB) + Gaming PC with RTX 4090 (24GB) = 48GB local pool for Qwen2.5-72B!

GPU Price Quotes

# Get prices for specific GPU type
terradev quote -g H100

# Filter by specific providers
terradev quote -g A100 -p runpod,vastai,lambda

# Quick-provision cheapest option
terradev quote -g H100 --quick

GPU Provisioning (Terraform-Powered)

# Provision single GPU via Terraform
terradev provision -g A100

# Provision multiple GPUs in parallel across providers
terradev provision -g H100 -n 4 --providers ["runpod", "vastai", "lambda", "aws"]

# Plan without applying
terradev provision -g A100 -n 2 --plan-only

# Set maximum price ceiling
terradev provision -g A100 --max-price 2.50

# Terraform state is automatically managed

Terraform Infrastructure Management

# Generate execution plan
terraform plan -config-dir ./my-gpu-infrastructure

# Apply infrastructure
terraform apply -config-dir ./my-gpu-infrastructure -auto-approve

# Destroy infrastructure  
terraform destroy -config-dir ./my-gpu-infrastructure -auto-approve

Kubernetes Clusters

# Create multi-cloud K8s cluster
terradev k8s create my-cluster --gpu H100 --count 4 --multi-cloud --prefer-spot

# List all clusters
terradev k8s list

# Get cluster details
terradev k8s info my-cluster

# Destroy cluster
terradev k8s destroy my-cluster

Inference Deployment

# Deploy model to InferX
terradev inferx deploy --model meta-llama/Llama-2-7b-hf --gpu-type a10g

# Check endpoint status
terradev inferx status

# List deployed models
terradev inferx list

# Get cost analysis
terradev inferx optimize

HuggingFace Spaces

# Deploy LLM template
terradev hf-space my-llama --model-id meta-llama/Llama-2-7b-hf --template llm

# Deploy with custom hardware
terradev hf-space my-model --model-id microsoft/DialoGPT-medium --hardware a10g-large --sdk gradio

# Deploy embedding model
terradev hf-space my-embeddings --model-id sentence-transformers/all-MiniLM-L6-v2 --template embedding

Instance Management

# View all running instances and costs
terradev status --live

# Stop instance
terradev manage -i <instance-id> -a stop

# Start instance
terradev manage -i <instance-id> -a start

# Terminate instance
terradev manage -i <instance-id> -a terminate

Analytics & Optimization

# Get 30-day cost analytics
terradev analytics --days 30

# Find cheaper alternatives
terradev optimize

Provider Setup

# Get quick setup instructions
terradev setup runpod --quick
terradev setup aws --quick
terradev setup vastai --quick

# Configure credentials (stored locally)
terradev configure --provider runpod
terradev configure --provider aws
terradev configure --provider vastai

Supported GPU Types

• H100 - NVIDIA H100 80GB (premium training)
• A100 - NVIDIA A100 80GB (training/inference)
• A10G - NVIDIA A10G 24GB (inference)
• L40S - NVIDIA L40S 48GB (rendering/inference)
• L4 - NVIDIA L4 24GB (inference)
• T4 - NVIDIA T4 16GB (light inference)
• RTX4090 - NVIDIA RTX 4090 24GB (consumer)
• RTX3090 - NVIDIA RTX 3090 24GB (consumer)
• V100 - NVIDIA V100 32GB (legacy)

Bug Fixes Applied

This release includes fixes for all known production issues:

All bugs are now resolved in v1.2.0

Terraform Integration

The MCP now includes a terraform.tf template for custom infrastructure:

terraform {
  required_providers {
    terradev = {
      source  = "theoddden/terradev"
      version = "~> 3.0"
    }
  }
}

resource "terradev_instance" "gpu" {
  gpu_type = var.gpu_type
  spot     = true
  count    = var.gpu_count
  
  tags = {
    Name        = "terradev-mcp-gpu"
    Provisioned = "terraform"
    GPU_Type    = var.gpu_type
  }
}

MoE Serving Architecture (v1.5)

Terradev v1.5 integrates the full MoE serving stack:

Supported Cloud Providers

RunPod, Vast.ai, AWS, GCP, Azure, Lambda Labs, CoreWeave, TensorDock, Oracle Cloud, Crusoe Cloud, DigitalOcean, HyperStack, Alibaba Cloud, OVHcloud, FluidStack, Hetzner, SiliconFlow, Baseten, HuggingFace, Paperspace

Environment Variables

Minimum setup:

• TERRADEV_RUNPOD_KEY: RunPod API key

Remote SSE mode:

• TERRADEV_MCP_BEARER_TOKEN: Bearer token for authenticating Claude.ai Connector requests (required in production)

Full multi-cloud setup:

• TERRADEV_AWS_ACCESS_KEY_ID, TERRADEV_AWS_SECRET_ACCESS_KEY, TERRADEV_AWS_DEFAULT_REGION
• TERRADEV_GCP_PROJECT_ID, TERRADEV_GCP_CREDENTIALS_PATH
• TERRADEV_AZURE_SUBSCRIPTION_ID, TERRADEV_AZURE_CLIENT_ID, TERRADEV_AZURE_CLIENT_SECRET, TERRADEV_AZURE_TENANT_ID
• Additional provider keys (VastAI, Oracle, Lambda, CoreWeave, Crusoe, TensorDock)
• HF_TOKEN: For HuggingFace Spaces deployment

Pricing Tiers

Enterprise+: Metered billing at $0.09 per GPU-hour with a minimum of 32 GPUs. Unlimited provisions, servers, seats, dedicated support, fleet management, and GPU-hour metering. Run terradev upgrade -t enterprise_plus.

Security

BYOAPI: All API keys stay on your machine. Terradev never proxies credentials through third parties.

Links

• GitHub
• PyPI (v3.7.0)
• NPM (v2.0.5)
• Docs